Search results for: Decision Feature

Authors: Ilyes Jenhani, Salem Benferhat, Zied Elouedi

Abstract:

This paper investigates the issue of building decision trees from data with imprecise class values where imprecision is encoded in the form of possibility distributions. The Information Affinity similarity measure is introduced into the well-known gain ratio criterion in order to assess the homogeneity of a set of possibility distributions representing instances-s classes belonging to a given training partition. For the experimental study, we proposed an information affinity based performance criterion which we have used in order to show the performance of the approach on well-known benchmarks.

Keywords: Data mining from uncertain data, Decision Trees, Possibility Theory.

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Authors: Leon Fu, Xiangyang Xue

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In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.

Keywords: DP, MDCT, MP3, QBH.

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Authors: R. Ferdous

Abstract:

Power, responsibility sharing, and democratic decision-making are the central ethos to co-management. It is assumed that involving local community in the decision-making process can create a sense of ownership and responsibility of that community and motivate the community towards collective action. But this paper demonstrated that the process to involve local community is not simple and straightforward as it is influenced by structural aspects, power relations among the actors, and social embedded institutions. These factors shape the process in that way who will participate, how they will participate and how the local community maneuvers their agency in the decision-making process. To grasp the complexities that materialize in the process of participation and to understand the inclusionary and exclusionary nature of participation, this paper examines the subjective understanding of different stakeholders concerning participation and furthermore observes the enabling or constraining factors that affect the community to exercise their agency.

Keywords: Participation, social embeddedness, power, structure.

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

Abstract:

Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: Approach instance-based, area Under the ROC Curve, Patient-specific Decision Path, clinical predictions.

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Authors: José M. Merigó, Pilar López-Jurado, M.Carmen Gracia, Montserrat Casanovas

Abstract:

We present a method for the selection of students in interdisciplinary studies based on the hybrid averaging operator. We assume that the available information given in the problem is uncertain so it is necessary to use interval numbers. Therefore, we suggest a new type of hybrid aggregation called uncertain induced generalized hybrid averaging (UIGHA) operator. It is an aggregation operator that considers the weighted average (WA) and the ordered weighted averaging (OWA) operator in the same formulation. Therefore, we are able to consider the degree of optimism of the decision maker and grades of importance in the same approach. By using interval numbers, we are able to represent the information considering the best and worst possible results so the decision maker gets a more complete view of the decision problem. We develop an illustrative example of the proposed scheme in the selection of students in interdisciplinary studies. We see that with the use of the UIGHA operator we get a more complete representation of the selection problem. Then, the decision maker is able to consider a wide range of alternatives depending on his interests. We also show other potential applications that could be used by using the UIGHA operator in educational problems about selection of different types of resources such as students, professors, etc.

Keywords: Decision making, Selection of students, Uncertainty, Aggregation operators.

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Authors: Kuo-Cheng Liu

Abstract:

In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.

Keywords: thresholding technique, tamper proofing, tamper recovery

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Authors: Turgay Temel, John Hallam

Abstract:

A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.

Keywords: Classification, neuro-spike coding, non-parametricmodel, parametric model, Gaussian mixture, EM algorithm.

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: A classifier, Algorithms decision tree, knowledge extraction, Support Vector Machine.

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Authors: Yanting Cao, Kazumitsu Nawata

Abstract:

As smartphones are continually upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described more refined, complex and detailed. In this context, we analyzed a set of experimental data, obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model become extremely challenging. After a series of feature selection and parameters adjustments, a well-performed SVM classifier has been trained. 

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Ferenc Peter Pach, Janos Abonyi

Abstract:

This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the a priori fuzzy clustering based partitioning of the continuous input variables. An application study is also presented, where the developed methods are tested on the well known Wisconsin Breast Cancer classification problem.

Keywords:

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.

Keywords: Anomaly detection, dimensionality reduction, frequencies selection, modal analysis, neural network, structural health monitoring, vibration measurement.

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Authors: C. Ardil

Abstract:

In this research, a multidimensional  compromise optimization method is proposed for multidimensional decision making analysis in the development ranking of the Gulf Cooperation Council Countries and Turkey. The proposed approach presents ranking solutions resulting from different multicriteria decision analyses, which yield different ranking orders for the same ranking problem, consisting of a set of alternatives in terms of numerous competing criteria when they are applied with the same numerical data. The multiobjective optimization decision making problem is considered in three sequential steps. In the first step, five different criteria related to the development ranking are gathered from the research field. In the second step, identified evaluation criteria are, objectively, weighted using standard deviation procedure. In the third step, a country selection problem is illustrated with a numerical example as an application of the proposed multidimensional  compromise optimization model. Finally, multidimensional  compromise optimization approach is applied to rank the Gulf Cooperation Council Countries and Turkey. 

Keywords: Standard deviation, performance evaluation, multicriteria decision making, multidimensional compromise optimization, vector normalization, multicriteria decision making, multicriteria analysis, multidimensional decision analysis.

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Authors: Sorin Soviany, Cristina Soviany, Mariana Jurian

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The paper presents a multimodal approach for biometric authentication, based on multiple classifiers. The proposed solution uses a post-classification biometric fusion method in which the biometric data classifiers outputs are combined in order to improve the overall biometric system performance by decreasing the classification error rates. The paper shows also the biometric recognition task improvement by means of a carefully feature selection, as much as not all of the feature vectors components support the accuracy improvement.

Keywords: biometric fusion, multiple classifiers

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Authors: Seongmin Yim, Hyun-Kyu Kang

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As a part of evaluation system for R&D program, the Korean government has applied feasibility analysis since 2008. Various professionals put forth a great effort in order to catch up the high degree of freedom of R&D programs, and make contributions to evolving the feasibility analysis. We analyze diverse R&D programs from various viewpoints, such as technology, policy, and Economics, integrate the separate analysis, and finally arrive at a definite result; whether a program is feasible or unfeasible. This paper describes the concept and method of the feasibility analysis as a decision making tool. The analysis unit and content of each criterion, which are key elements in a comprehensive decision making structure, are examined

Keywords: Decision Making of New Government R&D Program, Feasibility Analysis Study

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Authors: Hemashree Bordoloi, Kandarpa Kumar Sarma

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Rotation or tilt present in an image capture by digital means can be detected and corrected using Artificial Neural Network (ANN) for application with a Face Recognition System (FRS). Principal Component Analysis (PCA) features of faces at different angles are used to train an ANN which detects the rotation for an input image and corrected using a set of operations implemented using another system based on ANN. The work also deals with the recognition of human faces with features from the foreheads, eyes, nose and mouths as decision support entities of the system configured using a Generalized Feed Forward Artificial Neural Network (GFFANN). These features are combined to provide a reinforced decision for verification of a person-s identity despite illumination variations. The complete system performing facial image rotation detection, correction and recognition using re-enforced decision support provides a success rate in the higher 90s.

Keywords: Rotation, Face, Recognition, ANN.

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Authors: Laila Y. Fannas, Ahmed Y. Ben Sasi

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This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

Keywords: Signature Recognition, Artificial Neural Network, Angle Features.

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Authors: Nisha Singhal, Usha Chouhan

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At present, application of the extension of soft set theory in decision making problems in day to day life is progressing rapidly. The concepts of fuzzy soft set and its properties have been evolved as an area of interest for the researchers. The generalization of the concepts recently got importance and a rapid growth in the research in this area witnessed its vital-ness. In this paper, an application of the concept of generalized fuzzy soft set to make decision in a social problem is presented. Further, this paper also highlights some of the key issues of the related areas.

Keywords: Soft set, Fuzzy Soft set, Generalized Fuzzy Soft set, Membership and Non-Membership Score.

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Authors: Usman Qamar, Younus Javed

Abstract:

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and roughsets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: Rough-sets, Classification, Feature Selection, Entropy, Outliers, Frequent itemset mining.

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Authors: Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang

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Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.

Keywords: Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.

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Authors: J. Trarbach, S. Schosser, B. Vogt

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Decision-makers tend to prefer the first alternative over subsequent alternatives which is called the primacy effect. To reliably measure this effect, we conducted an experiment with real consequences for preference statements. Therefore, we elicit preferences of subjects using a rating scale, i.e. hypothetical preferences, and willingness to pay, i.e. real preferences, for two sequences of pain. Within these sequences, both overall intensity and duration of pain are identical. Hence, a rational decision-maker should be indifferent, whereas the primacy effect predicts a stronger preference for the first sequence. What we see is a primacy effect only for hypothetical preferences. This effect vanishes for real preferences.

Keywords: Decision making, primacy effect, real incentives, willingness to pay.

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Authors: Alexandru George Floares

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Chronic hepatitis B can evolve to cirrhosis and liver cancer. Interferon is the only effective treatment, for carefully selected patients, but it is very expensive. Some of the selection criteria are based on liver biopsy, an invasive, costly and painful medical procedure. Therefore, developing efficient non-invasive selection systems, could be in the patients benefit and also save money. We investigated the possibility to create intelligent systems to assist the Interferon therapeutical decision, mainly by predicting with acceptable accuracy the results of the biopsy. We used a knowledge discovery in integrated medical data - imaging, clinical, and laboratory data. The resulted intelligent systems, tested on 500 patients with chronic hepatitis B, based on C5.0 decision trees and boosting, predict with 100% accuracy the results of the liver biopsy. Also, by integrating the other patients selection criteria, they offer a non-invasive support for the correct Interferon therapeutic decision. To our best knowledge, these decision systems outperformed all similar systems published in the literature, and offer a realistic opportunity to replace liver biopsy in this medical context.

Keywords: Interferon, chronic hepatitis B, intelligent virtualbiopsy.

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Authors: Sanghyeok Oh, Keechul Jung

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Although lots of research work has been done for human pose recognition, the view-point of cameras is still critical problem of overall recognition system. In this paper, view-point insensitive human pose recognition is proposed. The aims of the proposed system are view-point insensitivity and real-time processing. Recognition system consists of feature extraction module, neural network and real-time feed forward calculation. First, histogram-based method is used to extract feature from silhouette image and it is suitable for represent the shape of human pose. To reduce the dimension of feature vector, Principle Component Analysis(PCA) is used. Second, real-time processing is implemented by using Compute Unified Device Architecture(CUDA) and this architecture improves the speed of feed-forward calculation of neural network. We demonstrate the effectiveness of our approach with experiments on real environment.

Keywords: computer vision, neural network, pose recognition, view-point insensitive, PCA, CUDA.

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Authors: Safdar Tanweer, Abdul Mobin, Afshar Alam

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The performance and analysis of speech recognition system is illustrated in this paper. An approach to recognize the English word corresponding to digit (0-9) spoken by 2 different speakers is captured in noise free environment. For feature extraction, speech Mel frequency cepstral coefficients (MFCC) has been used which gives a set of feature vectors from recorded speech samples. Neural network model is used to enhance the recognition performance. Feed forward neural network with back propagation algorithm model is used. However other speech recognition techniques such as HMM, DTW exist. All experiments are carried out on Matlab.

Keywords: Speech Recognition, MFCC, Neural Network, classifier.

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Ewa. M. Sztendur, Neil T. Diamond

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Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.

Keywords: Random Forests, Variable Importance, Fractional Factorial Designs, Student Attrition.

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Authors: Woei-Shyan Lee, Shuo-Ling Chang

Abstract:

The nanoindentation behaviour and phase transformation of annealed single-crystal silicon wafers are examined. The silicon specimens are annealed at temperatures of 250, 350 and 450ºC, respectively, for 15 minutes and are then indented to maximum loads of 30, 50 and 70 mN. The phase changes induced in the indented specimens are observed using transmission electron microscopy (TEM) and micro-Raman scattering spectroscopy (RSS). For all annealing temperatures, an elbow feature is observed in the unloading curve following indentation to a maximum load of 30 mN. Under higher loads of 50 mN and 70 mN, respectively, the elbow feature is replaced by a pop-out event. The elbow feature reveals a complete amorphous phase transformation within the indented zone, whereas the pop-out event indicates the formation of Si XII and Si III phases. The experimental results show that the formation of these crystalline silicon phases increases with an increasing annealing temperature and indentation load. The hardness and Young’s modulus both decrease as the annealing temperature and indentation load are increased.

Keywords: Nanoindentation, silicon, phase transformation, amorphous, annealing.

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Authors: Huei-Wen Lin, Huei-Fu Lu

Abstract:

Event sport tourism (EST) has become an especially important economic sector around the world. As the magnitude continues to grow, attracting more tourists, media, and investment for the host community, and many local areas/regions and states have identified the expenditures by visitors as a potential source of economic or employment growth. The main purposes of this study are to investigate stakeholders’ insights into the feature of hosting EST and using them as a regional development strategy. Continuing the focus of previous literature on the regional development and economic benefits by hosting EST, a total of fıve semi-structured interview questions are designed and a thematic analysis is employed to conduct with eight key sport and tourism decision makers in Atlanta during July to August 2016. Through the depth interviews, the study will contribute to a better understanding of stakeholders’ decision-making, identifying benefits and constraints as well as leveraging the impacts of hosting EST. These findings have provided stakeholders’ perspectives of hosting EST and using them as a reference of regional development in emerging sport tourism markets in the US. Additionally, this study examines key considerations and issues that affect and are critical to reliable understanding of the economic impacts of hosting EST on the regional development, and it will be able to benefit future management authorities (i.e. governments and communities) in their sport tourism development endeavors in defining and hosting successful EST. Furthermore, the insights gained from the qualitative analysis could help other cities/regions analyzing the economic impacts of hosting EST and using it as an instrument of city development strategy.

Keywords: Event sport tourism, regional economic development, thematic analysis, stakeholder.

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Authors: N. Praveen Kumar, Abhijit Mitra, C. Ardil

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Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.

Keywords: Decision feedback equalizer, Adaptive algorithm, Block based computation, Set membership filtering.

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Authors: Mohammed Nafie

Abstract:

Decision Feedback equalizers (DFEs) usually outperform linear equalizers for channels with intersymbol interference. However, the DFE performance is highly dependent on the availability of reliable past decisions. Hence, in coded systems, where reliable decisions are only available after decoding the full block, the performance of the DFE will be affected. A symbol based DFE is a DFE that only uses the decision after the block is decoded. In this paper we derive the optimal settings of both the feedforward and feedback taps of the symbol based equalizer. We present a novel symbol based DFE filterbank, and derive its taps optimal settings. We also show that it outperforms the classic DFE in terms of complexity and/or performance.

Keywords: Coding, DFE, Equalization, Exponential Channelmodels.

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