This paper presents a rule-based approach for the classification of power quality
disturbances. The disturbed signal is first characterized using the multi-resolution S …

In this paper, we review possible strategies for handling missing values in separate-and-
conquer rule learning algorithms, and compare them experimentally on a large number of …

Induction of decision rules plays an important role in machine learning. The main advantage
of decision rules is their simplicity and human-interpretable form. Moreover, they are …

In this paper we consider induction of rule-based classifiers from imbalanced data, where
one class (a minority class) is under-represented in comparison to the remaining majority …

The primary goal of the research reported in this paper is to identify what criteria are
responsible for the good performance of a heuristic rule evaluation function in a greedy top …

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Most commonly used inductive rule learning algorithms employ a hill-climbing search,
whereas local pattern discovery algorithms employ exhaustive search. In this paper, we …

In this paper a rule learning algorithm for the prediction of numerical target variables is
presented. It is based on the separate-and-conquer strategy and the classification phase is …

In this paper we propose a novel approach of rule learning called Relaxed Separate-and-
Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology …

In this paper, we review possible strategies for handling missing values in separate-and-
conquer rule learning algorithms, and compare them experimentally on a large number of …