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Comparing the Speed and Accuracy of Multi-Label Classification Models

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A fast and accurate multi-label classification method is needed to manage a rapidly increasing number of journal articles which can include more than one field of study. This research’s purposes are to compare the speed and accuracy of two multi-label classification models. The first model combines Label Powerset (LP), ReliefF (RF) and Fuzzy Similarity-based k-Nearest Neighbor (FSkNN). The second model combines LP, Distinguishing Feature Selector (DFS), and FSkNN. Speed is measured by training time and testing time consumed, while accuracy is measured using hamming loss. Based on the experiment, LP-DFS-FSkNN is faster and more accurate since its training time and hamming loss are less than LP-RF-FSkNN’s while the testing time of both models are the same.
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Multi-Label Classification; Machine Learning; Fuzzy Similarity-based k-Nearest Neighbor; ReliefF; Distinguishing Feature Selector

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