A Hybridized Adaptive Fruit Fly Optimization Based Online Sequential Extreme Learning Machine for Bio-Medical Data Classification

The objective of classification in high dimensional biomedical data is to map an input feature space to a predefined class labels with higher classification accuracy and less computational time. Although various machine learning techniques have been developed till now, still it is a challenging issue among researchers to develop accurate classifier for better diagnosis of various diseases. Therefore, in this study a new classifier named as adaptive fruit fly optimization (AFFO) based online sequential extreme learning machine (OSELM) is presented to classify biomedical data like Indian diabetes, Parkinson and Indian liver patient available in UCI repository. The proposed model performance is also compared with various models like AFFO-ELM and OSELM. The performances of these models are validated using some performance measures such as G-mean, sensitivity, F-Score, accuracy, specificity, and precision. The results confirm that AFFO-OSELM performance outperforms over other models used in this study.