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Probability based Extended Direct Attribute Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Manju, Ankit Kumar

Manju and Ankit Kumar. Probability based Extended Direct Attribute Prediction. International Journal of Computer Applications 155(5):41-44, December 2016. BibTeX

	author = {Manju and Ankit Kumar},
	title = {Probability based Extended Direct Attribute Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {5},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {41-44},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016912319},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper studies the object recognition along with the direct and indirect attribute prediction. The direct attribute prediction technique has been extended by using the probability based formulae. Moreover, information gain is also used to classify the object into different categories. The information gain is determined by using the entropy. The implementation of the work and comparison with existing DAP technique over YAHOO and pascal dataset signifies the effectiveness of the work.


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Dap, Iap, Probability, Sensitivity, Specificity.