Self-boosted with dynamic semi-supervised clustering method for imbalanced big data classification

Abstract

Big data plays a major role in the learning, manipulation, and forecasting of information intelligence. Due to the imbalance of data delivery, the learning and retrieval of information from such large datasets can result in limited classification outcomes and wrong decisions. Traditional machine learning classifiers successfully handling the imbalanced datasets still there is inadequacy in overfitting problems, training cost, and sample hardness in classification. To forecast a better classification, the research work proposed the novel “Self-Boosted with Dynamic Semi-Supervised Clustering Method”. The method is initially preprocessed by constructing sample blocks using Hybrid Associated Nearest Neighbor heuristic over-sampling to replicate the minority samples and merge each copy with every sub-set of majority samples to remove the overfitting issue thus slightly reduce noise with the imbalanced data. After preprocessing the data, massive data classification requires big data space which leads to large training costs. Therefore, the approach suggested a Heterogeneous Weight Ensemble classifier which calculates data space in each data sample by its nearest neighbors and adaptive weight adjustment to resolve the training cost with unstable sample problems. Subsequently, the classification acquires poor performance due to sample hardness. Thus the work introduced a Self-Managed Ensample Optimizer which separates the bulk of specimens into the bins according to their rigidity values and provides better classification results. Consequently, the proposed work effectively classifies the imbalanced dataset with high accuracy of 99%, to obtain balanced data with improved classification results.