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Classification based on Predictive Association Rule for Discrimination Prevention

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 19
Year of Publication: 2014
Authors:
Ankita K. Shinde
Pramod B. Mali
10.5120/15094-3339

Ankita K Shinde and Pramod B Mali. Article: Classification based on Predictive Association Rule for Discrimination Prevention. International Journal of Computer Applications 85(19):14-17, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Ankita K. Shinde and Pramod B. Mali},
	title = {Article: Classification based on Predictive Association Rule for Discrimination Prevention},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {19},
	pages = {14-17},
	month = {January},
	note = {Full text available}
}

Abstract

In data mining, discrimination is the subject which has been extensively studied in social and economic science. However, there are negative perceptions about data mining. Discrimination comes under two categories one is direct and second is indirect. Decisions based on sensitive attributes are termed as direct discrimination and the decisions which are based on non-sensitive attributes are termed as indirect discrimination which is strongly correlated with biased sensitive once. There are many new techniques proposed for solving discrimination prevention problems by applying direct or indirect discrimination prevention individually or both at the same time. New metrics to evaluate the utility were proposed and are compared with approaches. The proposed work discusses how privacy preservation and prevention between discrimination is implementing with the help of post processing approach. The Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification which is used to prevent discrimination prevention in post processing by improving accuracy.

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