There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS) technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS) based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM) as a classification approach, sequential forward selection (SFS) as a search strategy, and correlation feature selection (CFS) to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.
A Novel Battery State of Charge Estimation Based on the Joint Unscented Kalman Filter and Support Vector Machine Algorithms
