In this paper, we review the Wang-Mendel algorithm for the induction of fuzzy IF-THEN rules in the context of classification problems. A general fuzzy inference architecture for classification is proposed with the aim...
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In this paper, we review the Wang-Mendel algorithm for the induction of fuzzy IF-THEN rules in the context of classification problems. A general fuzzy inference architecture for classification is proposed with the aim of studying the influence of alternative configurations of the learning model. Specifically, we analyse the effects of changing the aggregation strategy, and we explore the use of different rule definitions, including or not the possibility to assign weighting factors to the generated rules. We test different rule weighting heuristics at this respect. The notion of rule conflict introduced in earlier versions of the algorithm is also reviewed in the context of the various resulting configurations of the fuzzy inference engine. A generalized version of the algorithm therefore results, bringing more flexibility to the configuration of the fuzzy inference engine, and improving the performance for certain problems. The main objective is to complement the results of previous approaches by offering a comprehensive overview of this popular algorithm for fuzzy rule induction in the context of classification problems. Several well-known machine learning classification benchmarks are analysed and compared looking for the best possible model configuration.
An equal matrix grammar is a parallel rewriting system. In this paper, we consider the problem of learning equal matrix grammars fromexamples. We introduce a learning method based on control sets and show two subclas...
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An equal matrix grammar is a parallel rewriting system. In this paper, we consider the problem of learning equal matrix grammars fromexamples. We introduce a learning method based on control sets and show two subclasses learnable in polynomial time with learning methods for regular sets. We also show that for any equal matrix language there exists an equal matrix grammar learnable efficiently from positive structural examples only.
We consider the problem of identifying the behavior of an unknown automaton with multiplicity in the field Q of rational numbers (Q-automaton) from multiplicity and equivalence queries. We provide an algorithm which i...
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We consider the problem of identifying the behavior of an unknown automaton with multiplicity in the field Q of rational numbers (Q-automaton) from multiplicity and equivalence queries. We provide an algorithm which is polynomial in the size of the Q-automaton and in the maximum length of the given counterexamples. As a consequence, we have that Q-automata are probably approximately correctly learnable (PAC-learnable) in polynomial time when multiplicity queries are allowed. A corollary of this result is that regular languages are polynomially predictable using membership queries with respect to the representation of unambiguous nondeterministic automata. This is important since there are unambiguous automata such that the equivalent deterministic automaton has an exponentially larger number of states.
The present paper studies a particular collection of classification problems, i.e., the classification of recursive predicates and languages, for arriving at a deeper understanding of what classification really is. In...
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The present paper studies a particular collection of classification problems, i.e., the classification of recursive predicates and languages, for arriving at a deeper understanding of what classification really is. In particular, the classification of predicates and languages is compared with the classification of arbitrary recursive functions and with their learnability. The investigation undertaken is refined by introducing classification within a resource bound resulting in a new hierarchy. Furthermore, a formalization of multi-classification is presented and completely characterized in terms of standard classification. Additionally, consistent classification is introduced and compared with both resource bounded classification and standard classification. Finally, the classification of families of languages that have attracted attention in learning theory is studied, too.
This paper examines the idea of incorporating machine learning algorithms into a database system for monitoring its stream of incoming queries and generating hierarchies with the most important concepts expressed in t...
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This paper examines the idea of incorporating machine learning algorithms into a database system for monitoring its stream of incoming queries and generating hierarchies with the most important concepts expressed in those queries. The goal is for these hierarchies to provide valuable input to the database administrator for dynamically modifying the physical and external schemas of a database for improved system performance and user productivity. The criteria for choosing the appropriate learning algorithms are analyzed, and based on them, two such algorithms, UNIMEM and COBWEB, are selected as the most suitable ones for the task. Standard UNIMEM and COBWEB implementations have been modified to support queries as input. Based on the results of experiments with these modified implementations, the whole approach appears to be quite promising, especially if the concept hierarchy from which the learning algorithms start their processing is initialized with some of the most obvious concepts captured in the database.
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