Okan Yilmaz

Date of Award

3-2002

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Gary B. Lamont, PhD

Abstract

We present a simple genetic algorithm (sGA), which is developed under Genetic Rule and Classifier Construction Environment (GRaCCE) to solve feature subset selection and weighting problem to have better classification accuracy on k-nearest neighborhood (KNN) algorithm. Our hypotheses are that weighting the features will affect the performance of the KNN algorithm and will cause better classification accuracy rate than that of binary classification. The weighted-sGA algorithm uses real-value chromosomes to find the weights for features and binary-sGA uses integer-value chromosomes to select the subset of features from original feature set. A Repair algorithm is developed for weighted-sGA algorithm to guarantee the feasibility of chromosomes. By feasibility we mean that the sum of values of each gene in a chromosome must be equal to 1. To calculate the fitness values for each chromosome in the population, we use K Nearest Neighbor Algorithm (KNN) as our fitness function. The Euclidean distance from one individual to other individuals is calculated on the d-dimensional feature space to classify an unknown instance. GRaCCE searches for good feature subsets and their associated weights. These feature weights are then multiplied with normalized feature values and these new values are used to calculate the distance between features.

AFIT Designator

AFIT-GCE-ENG-02M-05

DTIC Accession Number

ADA401635

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