@article{(Open Science Index):https://publications.waset.org/pdf/14323,
	  title     = {Improved Tropical Wood Species Recognition System based on Multi-feature Extractor and Classifier},
	  author    = {Marzuki Khalid and  RubiyahYusof and  AnisSalwaMohdKhairuddin},
	  country	= {},
	  institution	= {},
	  abstract     = {An automated wood recognition system is designed to
classify tropical wood species.The wood features are extracted based
on two feature extractors: Basic Grey Level Aura Matrix (BGLAM)
technique and statistical properties of pores distribution (SPPD)
technique. Due to the nonlinearity of the tropical wood species
separation boundaries, a pre classification stage is proposed which
consists ofKmeans clusteringand kernel discriminant analysis (KDA).
Finally, Linear Discriminant Analysis (LDA) classifier and KNearest
Neighbour (KNN) are implemented for comparison purposes.
The study involves comparison of the system with and without pre
classification using KNN classifier and LDA classifier.The results
show that the inclusion of the pre classification stage has improved
the accuracy of both the LDA and KNN classifiers by more than
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {5},
	  number    = {11},
	  year      = {2011},
	  pages     = {1495 - 1501},
	  ee        = {https://publications.waset.org/pdf/14323},
	  url   	= {https://publications.waset.org/vol/59},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 59, 2011},