Applications of Feature Selection Techniques on Large Biomedical Datasets

The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Markov Blanket Discovery, and Recursive Feature Elimination. We measured the efficiency of the selection, the stability of the algorithms, and the quality of the features chosen. Of the four techniques used, the Information Gain and Chi-Squared filters were the most efficient and stable. Both Markov Blanket Discovery and Recursive Feature Elimination took significantly longer to select features, and were less stable. The features selected by Recursive Feature Elimination were of the highest quality, followed by Information Gain and Chi-Squared, and Markov Blanket Discovery placed last. For the purpose of education (e.g. those in the biomedical field teaching data techniques), we recommend Information Gain or Chi-Squared filter. For the purpose of research or analyzing, we recommend one of the filters or Recursive Feature Elimination, depending on the situation. We do not recommend the use of Markov Blanket discovery for the situations used in this trial, keeping in mind that the experiments were not exhaustive.