Enhancing analysis of EEG image data sets using machine learning techniques: Dealing with class imbalances and data uncertainty

Abstract

Quality Assurance and output focused algorithm design is the trend, which is a current day research challenge. Inter- and intra- behavioral changes in the system influence on quality of outputs, this due to class imbalance problem prevailing in the data sets, that read uncertain values causes class imbalance problem (CIP). Predictive models that depend on such states of data sets become highly imbalanced. As the CIP on uncertain data starts from selection of samples and candidates for the algorithms; the selected random samples project high variation of classes, a very smaller class or even larger than expected. EEG image data sets are generated from the electro-encephalogram are always random and often never repeating because they vary coarsely based on subjects, ailments and various levels of voltages generated at various types of montage points. Irrespective of montage points and the voltage generated from the electro-encephalogram, the outcome of the algorithms is to develop a predictive model of the subject’s epileptical state. In this regard, the problem of class imbalance (CIP) shall be addressed and handled with various types of heuristic and sampling methods. We shall work on various types of sampling methods and heuristics that develop a predictive model on the uncertain data and draw comparisons to select based on the uncertain state of the data.