Rubina Aslam1,*, Manzoor Illahi Tamimy1, Waqar Aslam2
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086
Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking, Random k-label sets, and Hierarchy… More >
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