Ranking of decision rules with random power distribution

Ranking the Rules and Instances of Decision Trees

Traditionally, decision trees rank instances by using the local probability estimations for each leaf node. The instances in the same leaf node will be estimated with equal probabilities. In this paper, we propose a hierarchical ranking strategy by combining decision trees and leaf weighted Näıve Bayes to improve the local probability estimation for a leaf node. We consider the importance of th...

Ranking Cases with Classification Rules

Many real-world machine learning applications require a ranking of cases, in addition to their classi cation. While classi cation rules are not a good representation for ranking, the human comprehensibility aspect of rules makes them an attractive option for many ranking problems where such model transparency is desired. There have been numerous studies on ranking with decision trees, but not m...

Multiple criteria ranking and choice with all compatible minimal cover sets of decision rules

We introduce a new multiple criteria ranking/choice method that applies Dominance-based Rough Set Approach (DRSA) and represents the Decision Maker’s (DM’s) preferences with decision rules. The DM provides a set of pairwise comparisons indicating whether an outranking (weak preference) relation should hold for some pairs of reference alternatives. This preference information is structured using...

Monotonicity-based consensus states for the monometric rationalisation of ranking rules with application in decision making

Decision Tree with Better Ranking

AUC (Area Under the Curve) of ROC (Receiver Operating Characteristics) has been recently used as a measure for ranking performance of learning algorithms. In this paper, we present a novel probability estimation algorithm that improves the AUC value of decision trees. Instead of estimating the probability at the single leaf where the example falls into, our method averages probability estimates...