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Multi-class Classification

Multi-class or multi-nominal classification refers to the process of classifying the input samples into more than two classes. It is one of the Machine learning classification problems, recognizes one class for each sample from the multiple target labels from the knowledge of training data on multiple classes. It involves three approaches to solve the multi-class classification problems such as; Extension from Binary: Expanding the existing binary classifiers, Transformation to Binary: Conversion of multi-class classification problems to multiple binary classification problems either in the form of one vs. all or one vs. one, Hierarchical classification: Splitting up the outputs sets into tree or levels. Multi-layer perceptron, Decision Tree, K-nearest neighbors, Logistic Regression, Support Vector Machine, Neural Networks, Naive Bayes classifier, and Extreme learning are the popular multi-class classification algorithms. Character recognition, biometric identification, face recognition, and attack classification are emerging applications with multi-class classification problems. In particular, different categories of news classification, subjects based books classification, and streams based students classification are examples of multi-class classification.