Paper Info
Title
Performance evaluation of evolutionary algorithms in classification of biomedical datasets
Abstract
Biomedical datasets pose a unique challenge for machine learning and data mining techniques to extract accurate, comprehensible and hidden knowledge from them. In this paper, we comprehensively investigate the role of a biomedical dataset on the classification accuracy of an algorithm. To this end, we quantify the complexity of a biomedical dataset in terms of its missing values, imbalance ratio, noise and information gain. We have performed our experiments using six well-known evolutionary rule learning algorithms: XCS, UCS, GAssist, cAnt-Miner, SLAVE and Ishibuchi, on 31 publicly available biomedical datasets. The results of our experiments show that GAssist gives better classification accuracy among the compared schemes. However, the nature of a biomedical dataset -- not the selection of evolutionary algorithm -- plays a major role in determining the classification accuracy of a dataset. We further show that noise is a dominating factor in determining the complexity of a dataset and it is inversely proportional to the classification accuracy of all the algorithms. The complexity of biomedical dataset will prove useful to researchers in evaluating the classification potential of their dataset for automatic knowledge extraction.
Year
DOI
Venue
2009
10.1145/1570256.1570371
GECCO (Companion)
Keywords
Field
DocType
automatic knowledge extraction,biomedical dataset,major role,better classification accuracy,classification accuracy,classification potential,biomedical datasets,available biomedical datasets,evolutionary algorithm,performance evaluation,hidden knowledge,missing values,classification,machine learning,knowledge extraction,data mining,information gain
Data mining,Evolutionary algorithm,Computer science,Information gain,Artificial intelligence,Knowledge extraction,Missing data,Machine learning
Conference
Citations 
PageRank 
References 
6
0.47
20
Authors
2
Name
Order
Citations
PageRank
Ajay Kumar Tanwani1669.07
Muddassar Farooq2122183.47