Authors:
Petr Somol
1
;
Jana Novovicǒvá
1
and
Pavel Pudil
2
Affiliations:
1
Inst. of Information Theory and Automation, Czech Republic
;
2
Prague University of Economics, Faculty of Management, Czech Republic
Keyword(s):
Feature Selection, Subset Search, Search Methods, Performance Estimation, Classification Accuracy.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Enterprise Information Systems
;
Expert Systems
;
Health Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
One of the hot topics discussed recently in relation to pattern recognition techniques is the question of actual performance of modern feature selection methods. Feature selection has been a highly active area of research in recent years due to its potential to improve both the performance and economy of automatic decision systems in various applicational fields, with medical diagnosis being among the most prominent. Feature selection may also improve the performance of classifiers learned from limited data, or contribute to model interpretability. The number of available methods and methodologies has grown rapidly while promising important improvements. Yet recently many authors put this development in question, claiming that simpler older tools are actually better than complex modern ones – which, despite promises, are claimed to actually fail in real-world applications. We investigate this question, show several illustrative examples and draw several conclusions and recommendation
s regarding feature selection methods’ expectable performance.
(More)