in this article, a multi-objective memetic algorithm (ma) for rule learning is proposed. prediction accuracy and interpretation are two measures that conflict with each other. in this approach, we consider accuracy and interpretation of rules sets. additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. this...

A test of the predicted interaction between within-category discontinuity and verbal rule complexity on information-integration and rule-based category learning was conducted. Within-category discontinuity adversely affected information-integration category learning but not rule-based category learning. Model-based analyses suggested that some information-integration participants improved perfo...

-This paper describes a learning rule of neural networks via a simultaneous perturbation and an analog feedforward neural network circuit using the learning rule. The learning rule used here is a stochastic gradient-like algorithm via a simultaneous perturbation. The learning rule requires only forward operations o f the neural network. Therefore, it is suitable for hardware implementation. Fir...

In this paper, we provide a brief summary of elementary research in rule learning. The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing the given dataset so that predictions on new data can be made. We briefly review key learning tasks such as as...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

This paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in datasets both for rule learning and rule testing. In rule learning, the presence of these values affects the extension-against generalization operator in star generation, and the rule matching operator. I...