Performance evaluation of evolutionary algorithms in classification of biomedical datasets
Ajay Kumar Tanwani, Muddassar Farooq · 2009
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.