Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets - Saeed, Sana (2019)
Classification of imbalanced datasets remained a significant issue in data mining and machine learning (ML) fields. This research work proposed a new idea based on the optimization for handling the imbalanced datasets. A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). For its fast convergence and for its efficient search procedure, the self-adaptation is proposed in the parameters of the proposed hybrid algorithm. The effectiveness of this algorithm is verified by applying it on the unconstrained and constrained test functions through a simulation study. From the simulation study, it is shown that CSCMAES performed very well on each test function and produced the best values with minimum standard deviation and with faster convergence. Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. The proposed MOHA performed very well on these test functions. A new methodology is presented for the classification of the imbalanced datasets. The key idea of this methodology is to estimate the probabilities for each case in both classes separately. For this purpose, the normal distributions are applied to each class. The parameters of this distribution are optimized by applying the proposed MOHA. An efficient performance of this proposed methodology is observed with the help of an experimental study in which three types of datasets; simulated datasets, noisy borderline datasets and real-life imbalanced datasets are engaged.