PROMETHEE‐based hybrid feature selection technique for high‐dimensional biomedical data: application to Parkinson's disease classification

Abstract

Biomedical data are being collected for fields like cancer diagnosis and prognosis, brain signals, speech signals, genetic engineering to name a few. These data are very high dimensional these days, which makes it difficult to extract knowledge out of it through machine learning algorithms. In this work, the authors proposed a hybrid feature selection method based on a multi-attribute decision-making method PROMETHEE (preference ranking organisation method for enrichment evaluations) and Jaya optimisation algorithm. Their proposed method works in two phases. In the first phase, five filter methods are applied to get the ranking for each feature of the data set. In the second phase, all the five individual ranks are used as input choices for PROMETHEE which gives us a final rank for all the features. Then the top 3% features are selected for training the machine learning model. This technique is applicable for feature reduction in any high-dimensional biomedical data. Here, they have studied Parkinson's disease data set. The result shows that the proposed method improves the classification accuracy by 13.73% and that too in a minimum amount of time with a minimum number of features. Hence, this method can be used as an essential pre-processing step for high-dimensional biomedical data.