@article{(Open Science Index):https://publications.waset.org/pdf/10008694,
	  title     = {Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning},
	  author    = {Walid Cherif},
	  country	= {},
	  institution	= {},
	  abstract     = {Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {12},
	  number    = {3},
	  year      = {2018},
	  pages     = {170 - 175},
	  ee        = {https://publications.waset.org/pdf/10008694},
	  url   	= {https://publications.waset.org/vol/135},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 135, 2018},
	}