Faculty & staff

Title of Doctoral Dissertation: Qualitative Reasoning about Empirically Modelled Processes

 

Data Mining:

Also known as Machine Learning. My main interests are in various classification problems both from the theoretical point of view as well as in solving challenging real-world application problems.

On the theoretical side I am mostly interested in finding meters for comparing the predictive accuracy of data mining models. Many of those have been proposed over recent decades, but besides their benefits they do have some notable drawbacks (see for example in Arie Ben David, "What is wrong with hit ratio?" IEEE Intelligent Systems, 2006 (68-70), "A lot of randomness is hiding in accuracy", Engineering Applications of Artificial Intelligence, 20(7), 2007 (875-885) and also in U. Kaymak, A. Ben-David, R. Potharst, "The AUK: A simple alternative to the AUC", Int'l. Sci. J. Engineering Applications of Artificial Intelligence, February 2012)

 

In classification I am mostly interested in ordinal problems, where at least the output (and possibly some or all the attributes) is on an ordinal scale of measurements. See example in A. Ben-David, L. Sterling, T. Tran, "adding monotonicity to learning algorithms
may impair their accuracy" Expert Systems with Applications, 36(3), 2009 (6627-6634), and I. Milstein, A. Ben-David, R. Potharst, "About the sensitivity of ordinal classifiers to non-monotone noise", Artificial Intelligence Research, 4(2), 2015 (83-92).

 

In this area I am mainly interested in MIS project management. See D. Dvir, A. Ben-David, A. Sadeh, A.J. Shenhar, Critical Managerial Factors affecting Defense Projects Success: A Comparison between Neural-network and Regression Analysis, Engineering Applications of Artificial Intelligence , 19(5), 2006 (535-543), and also in A. Ben-David, R. Gelbard, I. Milstein, "Supplier ranking by multi-alternative proposal analysis for agile projects", Int'l J. of Project Management, 30, 2012 (723-730).