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

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Abstract

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 (Paper)
Uncontrolled Keywords:	machine learning; data mining; data mining optimization; nearest neighbour; distance matrix; classification; feature selection; genetic algorithm
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Mr. Mohd. Shamrie Sainin
Date Deposited:	30 Sep 2014 03:00
Last Modified:	30 Sep 2014 03:03
URI:	https://repo.uum.edu.my/id/eprint/12231

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