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Research Topics in Multi-label Classification

Research Topics in Multi-label Classification

   Multi-label classification is one of the classification problems, which refers that each instance in the sample being classified with multiple categories or more than one class label. With the categorization of each instance under the multiple target labels, the multi-label classification problem increases the complexity of the learning model. Popular methods to solve the multi-label classification problems are Problem transformation, Adaptive algorithm, and Ensemble Methods. Problem transformation performs conversion into binary or multi-class classification problems. The adaptive algorithm directly applies the learning algorithm to multi-label classification problems.
   The ensemble method produces optimal predictions by combining the prediction made by a set of multi-class classifiers through a voting scheme. Decision Trees, K-Nearest Neighbor, Naive Bayes, Gradient boosting, Random Forest, and Support Vector Machine have been widely applied algorithms for the Multi-Label classification problems. Several popular real-life application areas with multi-label classification problems include Face Recognition, Audio categorization, Medical Image Analysis, Bioinformatics, and Medical Data Analysis.