Analyzing Attribute Dependencies


Many effective and efficient learning algorithms assume independence of attributes. They often perform well even in domains where this assumption is not really true. However, they may fail badly when the degree of attribute dependencies becomes critical. In this paper, we examine methods for detecting deviations from independence. These dependencies give rise to “interactions” between attributes which affect the performance of learning algorithms. We first formally define the degree of interaction between attributes through the deviation of the best possible “voting” classifier from the true relation between the class and the attributes in a domain. Then we propose a practical heuristic for detecting attribute interactions, called interaction gain. We experimentally investigate the suitability of interaction gain for handling attribute interactions in machine learning. We also propose visualization methods for graphical exploration of interactions in a domain

Similar works

Full text

thumbnail-image time updated on 7/12/2013View original full text link

This paper was published in ePrints.FRI.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.