Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy rule based classifier as a problem of statis...

Since there is no individual approach that can be universally applied to effectively solve the hard problems of artificial intelligence and data analysis, hybrid systems are necessary to better tackle specific tasks by exploiting the advantages of different methodologies in a single framework. Based on known results of combining neural networks and rule-based systems, this work presents a hybri...

as a result of rule of law ideal, judges are required to act in accordance with laws. the ideal is justified by some political values: political liberty, legal liberty , personal liberty and equality. citizens are subject only to the law not to the arbitrary will or judgment of another who wields coercive government power. but in some cases law is indeterminate. in these cases there is an impor...

Combining multiple classi ers is an e ective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that signi cantly improve classi ers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classi er. In this paper, we present MFS, a combining a...

Combining multiple classiiers is an eeective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding, that signiicantly improve classiiers like decision trees, rule learners, or neural networks. Unfortunately, these combining methods do not improve the nearest neighbor classiier. In this paper, we present MFS, a...

Combining multiple classi ers is an e ective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding, that signi cantly improve classi ers like decision trees, rule learners, or neural networks. Unfortunately, these combining methods do not improve the nearest neighbor classi er. In this paper, we present MFS, a...

Combining multiple classiiers is an eeective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that signiicantly improve classiiers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classiier. In this paper, we present MFS, a combining a...

A Classification Association Rule (CAR), a common type of mined knowledge in Data Mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an “antecedent ⇒ consequent-class” rule. Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that build...

Among all sources of technical information, patent information is one the richest and most comprehensive. Knowing how to search in this mass documents becoming increasingly crucial. However, many users have limited knowledge patents strategies, so they must use intuitive, often approximate approaches that can lead highly inaccurate searches be time-consuming. To address problem, there are tools...

Classification Association Rule Mining (CARM) is an approach to classifier generation that builds an Association Rule Mining based classifier using Classification Association Rules (CARs). Regardless of which particular CARM algorithm is used, a similar set of CARs is always generated from data, and a classifier is usually presented as an ordered list of CARs, based on a selected rule ordering ...