Data Classification Using Feature Selection And kNN Machine Learning Approach

被引：26

作者：

Begum, Shemim ^{[1
]}

Chakraborty, Debasis ^{[2
]}

Sarkar, Ram ^{[3
]}

机构：

[1] Govt Coll Engn & Text Technol, Comp Sci & Engn, Berhampur, Murshidabad, India

[2] Murshidabad Coll Engn & Technol, Elect & Commn Engn Dept, Cossimbazar Raj, Msd, India

[3] Jadavur Univ, Comp Sci & Engn Dept, Kolkata, India

来源：

2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN) | 2015年

关键词：

Cancer Classification; Feature Selection; Consistency based feature selection(CBFS); Kernelized fuzzy rough set(KFRS); Fuzzy preferance based rough set(FPRS); ROUGH;

D O I：

10.1109/CICN.2015.165

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The k Nearest Neighbour (kNN) method is one of the most popular algorithm in clustering and data classification. The kNN algorithm founds to be performed very efficient in the experiments on different dataset. In this paper, we focus on the classification problem. The algorithm is experienced over Leukemia dataset. Initially three feature selection algorithm Consistency Based Feature Selection (CBFS), Fuzzy Preference Based Rough Set(FPRS) and Kernelized Fuzzy Rough Set(KFRS) is applied on the dataset and then kNN is applied as a classifier onto the dataset. The results of our experiment demonstrates that CBFS algorithm generally perform better than other two KFRS and FPRS algorithm respectively.

引用

页码：811 / 814

页数：4

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