The Borda Count was proposed as a method of ranking candidates by combining the rankings assigned by multiple voters. It has been studied extensively in the context of its original use in political elections and social choice-making. It has recently seen use in machine learning and in ranking web searches, but few of its formal properties have been extensively investigated. In this paper, we describe unsupervised, and (barely) supervised learning systems that employ the Borda Count as their underlying bases. We analyze the strengths and weaknesses of the technique in the context of landmine discrimination. We discuss and evaluate methods for algorithm fusion using several weighted Borda Count approaches and show how they affect algorithm fusion performance.

Date Published: 27 April 2007
PDF: 11 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655322 (27 April 2007); doi: 10.1117/12.722283

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)