A Novel Approach to Prevent the Discrimination in Data Mining
R. Kayalvizhi1, S.Sarika2
1R.Kayalvizhi, Department of Computer Science and Engineering, Sathyabama University, Chennai, India.
2S. Sarika, Department of Computer Science and Engineering, Sathyabama University, Chennai, India.
Manuscript received on January 26, 2014. | Revised Manuscript received on February 18, 2014. | Manuscript published on February 28, 2014. | PP: 407-409 | Volume-3, Issue-3, February 2014. | Retrieval Number: C2702023314/2013©BEIESP
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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: Automatic data collection has become the most wanted method in the banking sector to make automatic decisions like loan granting, denial. The discriminations in the dataset will lead to take the decisions in the partiality manner. The discrimination can be either direct or indirect discrimination. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on non-sensitive attributes. To overcome the partiality decisions the proposed system produces the anti-discrimination methodologies. The anti-discrimination methodologies prevent the discriminative decisions in the dataset. The proposed system prevents the discrimination without affecting the data quality.
Keywords: Anti-discrimination. Rule protection and rule generalization.