Maria Carolina Monard

Universidade de São Paulo

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Ronaldo C. Prati

Universidade Federal do ABC

Everton Alvares Cherman

Hospital Universitário da Universidade de São Paulo

Heloisa A. Camargo

Universidade Federal de São Carlos

Newton Spolaôr

Universidade Estadual do Oeste do Paraná

Marcos E. Cintra

Universidade Federal do Espírito Santo

Huei Lee

Universidade Estadual do Oeste do Paraná

André C. P. L. F. de Carvalho

Universidade de São Paulo

Gustavo Enrique de Almeida Prado Alves Batista

UNSW Sydney

Gustavo E. A. P. A. Batista

UNSW Sydney

Gustavo Batista

UNSW Sydney

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Universidad Católica Andrés Bello

232

Universidade de São Paulo

Universidad de la República

Brazilian Society of Computational and Applied Mathematics

Universidade Federal de São Carlos

Universidade Estadual de Campinas (UNICAMP)

Consejo Nacional de Investigaciones Científicas y Técnicas

Universidade Federal do Rio de Janeiro

Universidad de Montevideo

University of Buenos Aires

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Aplicações de inteligência artificial: uma visão geral

Anais (2000)

Maria Carolina Monard José Augusto Baranauskas

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Citations (1)

Table of content

Gabriel Lema Luis Martino Sebastián Berchesi Alicia Fernández Federico Lecumberry

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Content (measure theory)

10.1109/clei.2014.6965201

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Constructing ensembles of symbolic classifiers

Flávia Bernardini Maria Carolina Monard Ronaldo C. Prati

Learning algorithms are an integral part of the data mining (DM) process. However, DM deals with a large amount of data and most learning algorithms do not operate in massive datasets. A technique often used to ease this problem is related to data sampling and the construction of ensembles of classifiers. Several methods to construct such ensembles have been proposed. However, these methods often lack an explanation facility. This paper proposes methods to construct ensembles of symbolic classifiers. These ensembles can be further explored in order to explain their decisions to the user. These methods were implemented in the ELE system, also described in this work. Experimental results in two out of three datasets show improvement over all base-classifiers. Moreover, according to the obtained results, methods based on single rule classification might be used to improve the explanation facility of ensembles.

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10.1109/ichis.2005.31

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Citations (14)

Extracting Knowledge from Artificial Neural Networks: An Empirical Comparison of Trepan and Symbolic Learning Algorithms

Lecture notes in computer science (2002)

Cláudia Regina Milaré André C. P. L. F. de Carvalho Maria Carolina Monard

Representation

The Symbolic

10.1007/3-540-46016-0_29

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Citations (3)

Applying neural networks to determine vibration parameters in a turbine

Chandler Wellington Caulkins R.B.T. Oliveira André C. P. L. F. de Carvalho Solange Oliveira Rezende Maria Carolina Monard

Vibration signals analysis is considered as an appropriate diagnosis method for detecting faults. Several techniques have been used for detecting vibration signals. In this work, artificial neural networks (ANN) were used to predict vibration signals using process parameters measured in a turbine. The ANN models can be viewed as "black-boxes". One way to improve their comprehensibility is to use a symbolic model. As a first step in this direction, a hybrid rule-based regression model was also tested.

10.1109/ijcnn.1999.836203

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Missing Value Imputation Using a Semi-supervised Rank Aggregation Approach

Lecture notes in computer science (2008)

Edson Takashi Matsubara Ronaldo C. Prati Gustavo Batista Maria Carolina Monard

Imputation (statistics)

10.1007/978-3-540-88190-2_27

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Citations (11)

Um estudo sobre métodos de classificação hierárquica e medidas de avaliação

Jean Metz Alex A. Freitas Maria Carolina Monard