A filter based feature selection approach using lempel ziv complexity

Authors:
Sultan Uddin Ahmed;Md. Fazle Elahi Khan;Md. Shahjahan
Affiliations:
Dept. of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh;Dept. of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh;Dept. of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

Citing 17
Cited 0

Algorithmic information theory

Algorithmic information theory
An introduction to Kolmogorov complexity and its applications (2nd ed.)

An introduction to Kolmogorov complexity and its applications (2nd ed.)
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Unsupervised Feature Selection Using Feature Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Information, Randomness and Incompleteness

Information, Randomness and Incompleteness
An introduction to variable and feature selection

The Journal of Machine Learning Research
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation-based Feature Selection Strategy in Neural Classification

ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
FS_SFS: A novel feature selection method for support vector machines

Pattern Recognition
A hybrid approach for feature subset selection using neural networks and ant colony optimization

Expert Systems with Applications: An International Journal
Feature selection using correlation fractal dimension: Issues and applications in binary classification problems

Applied Soft Computing
Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Normalized mutual information feature selection

IEEE Transactions on Neural Networks
A robust extended Elman backpropagation algorithm

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Discriminative metric design for robust pattern recognition

IEEE Transactions on Signal Processing
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification

IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new filter based feature selection algorithm using Lempel-Ziv Complexity (LZC) measure, called 'Lempel Feature Selection' (LFS), is proposed. LZC finds the number of unique patterns in a time series. A time series is produced from the values of a feature and LZC of the feature is computed from the time series. The features are ranked according to their values of LZCs. Features with higher valued LZCs are selected and lower ones are deleted. LFS requires very less computation, since it just computes the LZCs of all features once. LFS is tested on several real world benchmark problems such as soybean, diabetes, ionosphere, card, thyroid, cancer, wine, and heart disease. The selected features are applied to a neural network (NN) learning model. NN produces better results with the selected features than that of randomly selected features.