An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples

Background: Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and techniques are widely used in cancer detection and prognosis. Methods: In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets sub-datasets, each of which contains a subset of the most relevant/ informative attributes of the original dataset. The MFSAC method is a selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. Results: To assess the performance of the proposed MFSAC-EC model, it is applied high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing establish its effectiveness with respect to other models. From the results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes. Subjects Bioinformatics, Data Mining and Machine Learning © Copyright 2021 Bose et al.