Applications of Multi-Label Classification
Ayesha Mariyam1, SK Althaf Hussain Basha2, S Vishwanadha Raju3
1Ayesha Mariyam, Department of CSE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
2SK Althaf Hussain Basha, Department of CSE, A1 Global Institute of Engineering and Technology, Markapur (Andhra Pradesh), India.
3S Vishwanadha Raju, Department of CSE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
Manuscript received on 25 February 2020 | Revised Manuscript received on 05 March 2020 | Manuscript Published on 15 March 2020 | PP: 86-89 | Volume-9 Issue-4S2 March 2020 | Retrieval Number: D10080394S220/2020©BEIESP | DOI: 10.35940/ijitee.D1008.0394S220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The absence of labels and the bad quality of data is a prevailing challenge in numerous data mining and machine learning problems. The performance of a model is limited by available data samples with few labels for training. These problems are ultra-critical in multi-label classification, which usually needs clean data. Multi-label classification is a challenging research problem that emerges in several applications such as multi-object recognition, text categorization, music categorization and image classification. This paper presents a literature review on multi-label classification, various evaluation metrics used for analyzing performance and research hchallenges.
Keywords: Multi-label Classification, Deep Learning, Machine Learning, Feature Extraction.
Scope of the Article: Classification