A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix

Mohd Shamrie Sainin and Rayner Alfred (2011) A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix. In: 2011 3rd Conference on Data Mining and Optimization, DMO 2011, 28-29 June 2011, Putrajaya, Malaysia .

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Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI Machine Learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Classification, Data mining, Data mining optimization, distance matrix, Feature selection, Genetic algorithm, machine learning, Nearest neighbour
Subjects: Q Science > QA Mathematics
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 13 Aug 2012 07:57
Last Modified: 08 Sep 2014 07:01
URI: http://eprints.ums.edu.my/id/eprint/4742

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