Applying Machine Learning For Automated Classification Of Biomedical Data In Sub

This Book Describes Efforts To Improve Subject-independent Automated Classification Techniques Using A Better Feature Extraction Method And A More Efficient Model Of Classification. It Evaluates Three Popular Saliency Criteria For Feature Selection, Showing That They Share Common Limitations, Including Time-consuming And Subjective Manual De-facto Standard Practice, And That Existing Automated Efforts Have Been Predominantly For Subject Dependent Setting. It Then Proposes A Novel Approach For Anomaly Detection, Demonstrating Its Effectiveness And Accuracy For Automated Classification Of Biomedical Data, And Arguing Its Applicability To A Wider Range Of Unsupervised Machine Learning Applications In Subject-independent Settings.