Harmony-based feature selection to improve the nearest neighbor classification

Authors:
Ali Adeli;Mehrnoosh Sinaee;Javad Zomorodian;Mehdi Neshat
Affiliations:
Bojnurd Darolfonoun Technical College, Bojnurd, Iran;University of Applied Science & Technology, Sirjan, Iran;Shiraz Bahonar Technical College, Shiraz, Iran;Islamic Azad University, Shirvan, Iran
Venue:
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Year:
2012

Citing 10
Cited 0

Floating search methods in feature selection

Pattern Recognition Letters
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Support Vector Machines by Means of Genetic Algorithms

ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering

IEEE Transactions on Knowledge and Data Engineering
Supervised feature selection via dependence estimation

Proceedings of the 24th international conference on Machine learning
Genetic programming for simultaneous feature selection and classifier design

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving nearest neighbor classification by elimination of noisy irrelevant features

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
On the effectiveness of receptors in recognition systems

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new approach for feature selection is presented in this paper. The proposed approach uses the Harmony Search with a novel fitness function to eliminate noisy and irrelevant features. Harmony vectors contain real weights which refer to feature space. The best and significant features are selected according to a threshold. Fitness function of Harmony Search is based on the Area Under the receiver operating characteristics Curve (AUC). All of the selected features are employed to improve the classification of the k Nearest Neighbor (k-NN) classifier. Experimental results claim that the proposed method is able to improve the classification performance of k-NN algorithm in comparison with the other important methods in realm of feature selection such as BAHSIC, FSS, BSS and MFS.