Dimensionality reduction via genetic value clustering

Authors:
Alexander Topchy;William Punch
Affiliations:
Computer Science Department, Michigan State University, East Lansing, MI;Computer Science Department, Michigan State University, East Lansing, MI
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 10
Cited 2

Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms and Grouping Problems

Genetic Algorithms and Grouping Problems
Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms
Feature Space Transformation Using Genetic Algorithms

IEEE Intelligent Systems
Further Research on Feature Selection and Classification Using Genetic Algorithms

Proceedings of the 5th International Conference on Genetic Algorithms
Likelihood Computations Using Value Abstraction

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Multivariate Information Bottleneck

UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Dimensionality reduction using genetic algorithms

IEEE Transactions on Evolutionary Computation
Automatic facial feature extraction by genetic algorithms

IEEE Transactions on Image Processing

Preventing conflict situations during authorization

WSEAS Transactions on Computers
Security concerns for web services and applications

ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature extraction based on evolutionary search offers new possibilities for improving classification accuracy and reducing measurement complexity in many data mining and machine learning applications. We present a family of genetic algorithms for feature synthesis through clustering of discrete attribute values. The approach uses new compact graph-based encoding for cluster representation, where size of GA search space is reduced exponentially with respect to the number of items in partitioning, as compared to original idea of Park and Song. We apply developed algorithms and study their effectiveness for DNA fingerprinting in population genetics and text categorization.