This paper presents an adaptive self-organizing concurrent system (ASOCS) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (A...

As an alternative to the conventional Hebb-type unsupervised learning, differential learning was studied in the domain of Hebb’s rule [1] and decorrelation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as differential ICA. We derive a differential ICA algorithm in the framework of maximum likelihood estimation and random walk model. Algorithm der...

Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed ...

Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed ...

This paper describes ICN, an incremental version of the CN2 rule learning system. Unlike other incremental rule learning systems which learn rules gradually, adding and removing conditions in a hill-climbing search, ICN learns or unlearns each rule “all at once,” using beam search as in CN2. In batch training and testing with the forest cover prediction problem, ICN performs nearly as well as C...

This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (A...

ABSTRACT AND CONCLUSION NEEDS TO BE RE-WRITTEN. ESPECIALLY WE SHOULD EMPHASIZE OUR CONTRIBUTION AND ORGINALITY OF THE WORK IN CONCLUSION. In this paper we describe the ILA-2 rule induction algorithm from the machine learning domain. ILA2 is the improved version of a novel inductive learning algorithm, namely ILA. We first describe the basic algorithm ILA, then present how the algorithm was impr...

In this paper, we show how two-objective genetic algorithms can be applied to a rule selection problem of linguistic classification rules. First we briefly describe a generation method of linguistic classification rules from numerical data. Next we formulate a rule selection problem of linguistic classification rules. This problem has two objectives: to maximize the number of correctly classifi...

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However ...

We introduce a rule selection algorithm called ROCCER, which operates by selecting classification rules from a larger set of rules – for instance found by Apriori – using ROC analysis. Experimental comparison with rule induction algorithms shows that ROCCER tends to produce considerably smaller rule sets with compatible Area Under the ROC Curve (AUC) values. The individual rules that compose th...