The Role of Biomedical Dataset in Classification

  • Ajay Kumar Tanwani
  • Muddassar Farooq
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

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 – not a particular choice of algorithm – play a major role in determining the classification accuracy. In the end, we provide researchers with a meta-model and an empirical equation to estimate the classification potential of a dataset on the basis of its complexity. This well help researchers to efficiently pre-process the dataset for automatic knowledge extraction.

Keywords

Classification Complexity Measures Biomedical Datasets 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ajay Kumar Tanwani
    • 1
  • Muddassar Farooq
    • 1
  1. 1.Next Generation Intelligent Networks Research Center (nexGIN RC)National University of Computer & Emerging Sciences (FAST-NUCES)IslamabadPakistan

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