Noise Removal Process from Label Classification using Machine Learning
Mokshada Kotwal1, Shraddha Khonde2
1Mokshada Kotwal, Computer Department, M.E.S College of Engineering, Pune, India.
2Prof. Shraddha Khonde, Computer Department, M.E.S College of Engineering, Pune, India.
Manuscript received on 10 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 172-175 | Volume-8 Issue-3 September 2019 | Retrieval Number:C3920098319/19©BEIESP | DOI: 10.35940/ijrte.C3920.098319
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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: Text classification and clustering approach is essential for big data environments. In supervised learning applications many classification algorithms have been proposed. In the era of big data, a large volume of training data is available in many machine learning works. However, there is a possibility of mislabeled or unlabeled data that are not labeled properly. Some labels may be incorrect resulted in label noise which in turn regress learning performance of a classifier. A general approach to address label noise is to apply noise filtering techniques to identify and remove noise before learning. A range of noise filtering approaches have been developed to improve the classifiers performance. This paper proposes noise filtering approach in text data during the training phase. Many supervised learning algorithms generates high error rates due to noise in training dataset, our work eliminates such noise and provides accurate classification system.
Index Terms: Label Noise, Majority Voting, Unlabeled, Supervised Learning.
Scope of the Article: Classification