• Prashant Mahakal
• Prof. Pritesh Jain

ABSTRACT: Cost-sensitive classification considers the varying costs of particular incorrect sorting. Both cost-sensitive classification and online learning have been broadly researched in data mining and machine learning communities, respectively. However, very restricted study addresses an essential intersecting issue, that is, “Cost-Sensitive Online Classification". Cost of what is misclassified is definitely not considered for the measuring execution in general methodologies; cost sensitive classification considers expense of the misclassified label. In this paper we proposed, formally study this issue, and propose a new framework for Cost-Sensitive Online Classification by directly surging cost-sensitive symptoms implementing online gradient descent methods. malicious Uniform Resource Locator (URL) detection is an essential issue in web search and mining, which plays a complex role in internet protection.

"A Process For Online Dynamic Learning With Cost Sensitivity In Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 12, page no.156-159, December-2016, Available :http://www.jetir.org/papers/JETIR1612023.pdf

"A Process For Online Dynamic Learning With Cost Sensitivity In Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 12, page no. pp156-159, December-2016, Available at : http://www.jetir.org/papers/JETIR1612023.pdf