Sequential Pattern Mining for Enhanced Multi-Label Classification
Keywords:
Sequential Pattern Mining, Technique, Bioinformatics, Classification, Multi Label, SimulationAbstract
Sequential Pattern Mining (SPM) with Multi-Label Classification is a fascinating area in data mining and machine learning. It combines extracting patterns from sequential data with handling datasets where each instance is associated with multiple labels. The goal of classification is to provide the most precise prediction of an unseen instance class. A variation of single-label classification, multi-label classification uses a collection of labels linked to a single occurrence. Text classification, functional genomics, picture classification, music categorization, etc. are some examples of recent applications that use multi label classification. This article presents the subject of multi-label classification, several techniques for it, and an evaluation measures for it. Additionally, conducted comparative research of multi-label classification algorithms using both theoretical studies and simulations on different datasets.
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