Ensemble learning methods for pay-per-click campaign management. Issue 10 (15th June 2015)

• Record Type: Journal Article
• Authors: King, Michael A.; Abrahams, Alan S.; Ragsdale, Cliff T.
• Is Part Of: Expert systems with applications. Volume 42:Issue 10(2015)
• Page Start: 4818
• Page End: 4829
• Publication Date: 2015-06-15
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.01.047
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st135">Highlights</title> <sec> <p id="sp0005"> <list id="l0005"> <list-item id="o0005"> <label></label> <p id="p0300">We propose using ensemble techniques to improve pay-per-click campaign management.</p> </list-item> <list-item id="o0010"> <label></label> <p id="p0305">Actual data containing pay-per-click campaign results is used for our analysis.</p> </list-item> <list-item id="o0015"> <label></label> <p id="p0310">Four base classifiers are analyzed as inputs to four ensemble modeling techniques.</p> </list-item> <list-item id="o0020"> <label></label> <p id="p0315">A MetaCost ensemble predicted the highest campaign portfolio profit.</p> </list-item> </list> </p> </sec> </abstract>