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Multiobjective genetic fuzzy systems (MGFSs) have proved to be very effective in classification, regression and control tasks. However, large scale problems still present open and challenging research issues. Making identification of fuzzy rules faster can enlarge the range of applications of MGFSs. In this work we first analyze the time complexity for both the identification and the evaluation of...
In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify...
This paper presents and evaluates a new version of the Semantic Mapping method applied to the construction of a hybrid document organization system based on Self-Organizing Maps. The hybrid system uses reduced document vectors generated by Semantic Mapping to training the SOM map, thus reducing the training time without compromising the quality of the document map. We test the hybrid system with different...
In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounded complexity. We propose a method to generate synthetic data sets with a specific complexity, based on the length of the class boundary. We design a genetic algorithm as a search technique and find it useful to obtain class labels according to the desired complexity. The results show the suitability...
Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. This...
This paper addresses the problem of probability estimation in multiclass classification tasks combining two well known data mining techniques: support vector machines and neural networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs support vector machines within a one-vs-all reduction from multiclass to binary approach to obtain the distances...
One of the main kinds of computational tasks regarding gene expression data is the construction of classifiers (models), often via some machine learning (ML) technique and given data sets, to automatically discriminate expression patterns from cancer (tumor) and normal tissues or from subtypes of cancers. A very distinctive characteristic of these data sets is its high dimensionality and the fewer...
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