Modified ML-kNN and Rank SVM for Multi-label Pattern Classification
| Autor: | Thanseeha Kassim, B. S. Shajee Mohan, K. V. Ahammed Muneer |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1921:012027 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1921/1/012027 |
| Popis: | To develop an efficient multi-label classifier is the main objective of this paper. In multi-label learning tasks such as classification, each example is associated with a set of labels, and the task is to predict the label set whose size is unknown apriory for each unseen example. In a realistic scenario each object or entity belongs to a multi-label category. Multi-Label k-Nearest Neighbor (ML-kNN), Rank-SVM (Ranking Support Vector Machine) are two popular techniques used for multi-label pattern classification. ML-kNN is a multi-label version of standard kNN and Rank SVM is a multi-label extension of standard SVM. The main aim of this work is to enhance the performance of these methods. Multi-label classifiers generally consider ranking loss, Hamming loss, one error, average precision and coverage as a performance metrics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|