Relational rule learning is typically used in solving classification and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup disco...

Relational rule learning is typically used in solving classification and prediction tasks. However, it can also be adapted to the description task of subgroup discovery. This paper takes a propositionalization approach to relational subgroup discovery (RSD), based on adapting rule learning and first-order feature construction, applicable in individualcentered domains. It focuses on the use of c...

The thermal perceptron is a simple extension to Rosenblatt’s perceptron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, To, has been found, then the thermal perceptron outperforms the Pocket algorithm and methods based on gra...

One of the known classification approaches in data mining is rule induction (RI). RI algorithms such as PRISM usually produce If-Then classifiers, which have a comparable predictive performance to other traditional classification approaches such as decision trees and associative classification. Hence, these classifiers are favourable for carrying out decisions by users and hence they can be uti...

In many application domains, there is a need for learning algorithms that generate accurate as well as comprehensible classifiers. In this paper, we present TRIPPER a rule induction algorithm that extends RIPPER, a widely used rule-learning algorithm. TRIPPER exploits knowledge in the form of taxonomies over the values of features used to describe data. We compare the performance of TRIPPER wit...

An investigation of rule learning processes that allow the inclusion of negated features is described. The objective is to establish whether the use of negation in inductive rule learning systems is effective with respect to classification. This paper seeks to answer this question by considering two issues relevant to such systems; feature identification and rule refinement. Both synthetic and ...

The delta rule of associative learning has recently been used in several models of human category learning, and applied to categories with different relative frequencies, or base rates. Previous research has emphasized predictions of the delta rule after extensive learning. Our first experiment measures the relative acquisition rates of categories with different base rates, and the delta rule s...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...