Júlio C. Nievola

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In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership(More)
We use a standard tree-structure representation for each individual. The GP constructs new attributes out of the continuous (real-valued) attributes of the data set being mined. Each individual corresponds to a candidate new attribute. The terminal set consists of all the continuous attributes in the data being mined. The function set consists of four(More)
The main objective of this work is to present an exploratory approach on electroencephalographic (EEG) signal, analyzing the patterns on the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining(More)
Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents an algorithm for hierarchical classification using the global approach, called(More)
The predictive accuracy obtained by a classification algorithm is strongly dependent on the quality of the attributes of the data being mined. When the attributes are little relevant for predicting the class of a record, the predictive accuracy will tend to be low. To combat this problem, a natural approach consists of constructing new attributes out of the(More)
This work proposes a trainable system for summarizing news and obtaining an approximate argumentative structure of the source text. To achieve these goals we use several techniques and heuristics, such as detecting the main concepts in the text, connectivity between sentences, occurrence of proper nouns, anaphors, discourse markers and a binary-tree(More)
The families of G-Protein Coupled Receptor (GPCR) and enzymes are among the main protein family. They represent to the scientific and medical communities, a significant target for bioactive and drug discovery programs. The model of classification of enzymes and GPCR is characterized by its hierarchical structure in format of tree and this makes more(More)
A common problem in KDD (Knowledge Discovery in Databases) is the presence of noise in the data being mined. Neural networks are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. However, they have the wellknown disadvantage of not discovering any high-level rule that can be used as a support for human decision(More)