Time Conserving Multi-Label Classification System by Incorporating Pyramid Data Structure
Y. Jeyasheela1, S.H. Krishnaveni2
1Y. Jeyasheela*, Department of Information Technology, Noorul Islam Centre for Higher Education, Kumaracoil, Nagercoil, India.
2Dr. S.H. Krishnaveni, Department of CSE, Baselios Mathew II College of Engineering, Kerala, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1379-1384 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7627088619/2019©BEIESP | DOI: 10.35940/ijeat.F7627.088619
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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: Data classification is one of the evergreen research areas of data analysis. Numerous data classification approaches exist in the literature and most of the classification systems are based on binary and multi-class classification. Multi-label classification system attempts to suggest multiple labels for a single entity. However, it is complex to attain a better multi-label classification system. Taking this as a challenge, this work proposes a multi-label classification system, which extracts the features of both entities and labels. The relationship between them are organised in the pyramid data structure. As the features are organized effectively, the interrelated labels are present in the same tier. This feature makes it simple for suggesting multiple labels for a single entity. The performance of this work is analysed over three different datasets and compared against existing approaches in terms of precision, recall, accuracy and time consumption.
Keywords: About Data Classification, multi-label classification, pyramid data structure.