Masayo Tsurumi

LEM2-based rule induction via clustering decision classes

In this paper, it is proposed to cluster decision classes before applying a rule induction method. The similarity between decision classes is defined and an agglomerative hierarchical clustering method is applied. At each branch of the obtained dendrogram, LEM2, one of frequently used rule induction algorithm, is applied to induce decision rules inferring clusters. In such a way, a set of decision rules classifying objects into decision classes is obtained. The performance of the proposed method is compared with the direct application of LEM2 to each decision class by a numerical experiment.

LEM2-based rule induction from data tables with imprecise evaluations

We propose a rough set approach to data tables with imprecise evaluations. Treatments of imprecise evaluations are described and four rule induction schemas are proposed. For each rule induction schema, we apply LEM2-based rule induction algorithm by defining the positive object set appropriately. The performances of the proposed rule induction algorithms are examined by numerical experiments.

Pseudo-Banzhaf Values in Bicooperative Games

In conventional cooperative games, it is assumed that each player will choose whether he/she joins a coalition or not. In bicooperative games defined by Bilbao, each player is allowed to choose one among three alternatives such as to be a defender, to be a fence-sitter and to be a defeater. In this paper, we define a solution which is the probabilistic value of a player when coalitions not containing him/her are equally likely to arise for a bicooperative game. We call this solution the pseudo-Banzhaf value on the class of bicooperative games. Axiomatizations to show rationality of the solution are given. Further, we formulate a weighted majority game as a bicooperative game and give a numerical example

Lazy classification using dominance-based rough membership values

In this paper, estimation methods for decision attribute values are investigated based on the variable-precision dominance-based rough set model. The conceivable approaches are shown and the idea of k-nearest neighbor algorithm is introduced to reduce the computation time. It is shown by numerical experiments that the proposed method together with k-nearest neighbor algorithm is advantageous in both accuracy and computation time over the conventional estimation through rule induction.