In this paper we present a new approach to handling incomplete information and classifier complexity reduction. We describe a method, called DRJ, that performs data decomposition and decision rule joining to avoid the necessity of reasoning with missing attribute values. In the consequence more complex reasoning process is needed than in the case of known algorithms for induction of decision ru...

In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...

In many real application areas, the data used are highly skewed and the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this paper is to improve the performance of fuzzy rule based classification systems on imb...

© Fast, Cheap and In Control: A Step Towards Pain Free Security! Sandeep Bhatt, Cat Okita, Prasad Rao HP Laboratories HPL-2008-111 firewall, network, security metrics We hypothesize that it is possible to obtain significant gains in operational efficiency through the application of simple analysis techniques to firewall rule sets. This paper describes our experiences with a firewall analysis to...

The ML Revolution in NLP The plot on the slide shows the percentage of papers at the main ACL conference which report research on statistical NLP. Today, in 2015, the figure would be close to 100%. Before 1990, research in NLP was rule-based, where the rules were written by domain experts (for example translators, for machine translation). The limitations of rule-based systems are well-document...

Association rule mining is one of the most important technique in data mining. Which wide range of applications It aims it searching for intersecting relationships among items in large data sets and discovers association rules. The important of association rule mining is increasing with the demand of finding frequent patterns from large data sources. The exploitation of frequent item set has be...

Decision rules, which can provide good interpretability and flexibility for data mining tasks, have received very little attention in the stream mining community so far. In this work we introduce a new algorithm to learn rule sets, designed for open-ended data streams. The proposed algorithm is able to continuously learn compact ordered and unordered rule sets. The experimental evaluation shows...

This paper proposes a fuzzy beam search rule induction algorithm for the classification task. The use of fuzzy logic and fuzzy sets not only provides us with a powerful, flexible approach to cope with uncertainty, but also allows us to express the discovered rules in a representation more intuitive and comprehensible for the user, by using linguistic terms (such as low, medium, high) rather tha...

Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rulebased classifiers. It is, however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple but effective idea to improve the scalability of genetic fuzzy rule selection to large data sets. Our idea is based on its parallel distributed impleme...

The paper presents two rough sets based filtering approaches combined with rule based classifiers suited for handling imbalanced data sets, i.e., data sets where the minority class of primary importance is under-represented in comparison to the majority classes. We introduced two techniques to detect and process inconsistent majority cases in the boundary between the minority and majority class...