An Ensemble Methods of Predicting the New Labels with Concept Drift from a High-Dimensional Data Stream

Abstract

Multi-Label Learning (MLL) has arisen in data engineering to identify instances based on a specific feature associated with a collection of labels. Adaptive learning necessitates classifying features with New Labels (NLs) if a data stream contains newer perspectives. As a result, an MLL with Emerging Multiple NLs (MuEMNL) and managing High-Dimensional data streams (MuEMNLHD) approaches were developed that divides the NL sets into multiple NLs for efficient classification. However, it did not handle concept drift issues when huge amounts of data arrived at high speeds using limited resources. Hence, this article proposes an adaptive ensemble learning approach to cope with a huge amount of data streams and solve concept drift issues by constructing a MuEMNL-Ensemble Neural Network (ENN) rather than a random forest classifier. It defines the number of NNs in the ensemble, whether or not they use constructive pruning, how many hidden nodes each NN uses, and how many training samples are used to train each NN independently. Also, to solve the concept drifts, pairwise and non-pairwise diversity measures are analyzed while constructing ensemble NN for efficient training using the entire learning examples. Moreover, the tradeoff between the NN’s precision and diversity is maintained simultaneously. At last, the test outcomes reveal that the proposed approach attains a better performance contrasted with the existing MLL approaches.