The Role of Biomedical Dataset in Classification

In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complexity measures: correlation-based feature selection subset merit, noise, imbalance ratio, missing values and information gain. The effect of these complexity measures on classification accuracy is evaluated using five diverse machine learning algorithms: J48 (decision tree), SMO (support vector machines), Naive Bayes (probabilistic), IBk (instance based learner) and JRIP (rule-based induction). The results of our experiments show that noise and correlation-based feature selection subset merit

• Type: Conference
• Year: 2009
• Where: AIME
• Authors: Ajay Kumar Tanwani, Muddassar Farooq
• Keywords: AIME 2009, Artificial Intelligence, Classification Accuracy, Correlation-based Feature Selection, Selection Subset Merit