An Improved Hybrid Stacked Classifier For Multi Label Text Categorization
P. Sree Lakshmi1, Kavitha2
1P.Sree Lakshmi , School of Computer Science and Applications, REVA University, Bangalore, India.
2Kavitha , School of Computer Science and Applications, REVA University, Bangalore, India.
Manuscript received on 05 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 5911-5915 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4739098319/19©BEIESP | DOI: 10.35940/ijrte.C4739.098319
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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: Nowadays, the applications of multi label classification are increasing very rapidly with the growth of information technology. One among it is, multi label text categorization which deals with the automatic categorization of documents or comments posted in a social site. Because of the exponential growth of digitization of unstructured categorical data, there is an emerging need for text categorization in particular with multiple labels. Conventionally, it has been solved by either transforming the problem into single class or extending the existing classifiers. An improved hybrid stacked classifier has been proposed to address the challenges in multiple label assignment for text document. The model has been built with three classifiers stacked together with Label Power set by taking class probabilities and a Meta classifier. The experimental results show that the proposed method outperforms well than the existing methods.
Index Terms: Multi Label Text Categorization, Multi Label Classification, Stacked Classifier, Label Power Set, Hybrid Classifier
Scope of the Article: Classification