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Authors: Genival Pavanelli ; Maria Teresinha Arns Steiner ; Alessandra Memari Pavanelli and Deise Maria Bertholdi Costa

Affiliation: Federal University of Paraná State UFPR, Brazil

Keyword(s): Mathematical Programming, Artificial Neural Networks, Multiple Linear Regression, Decision Tree, Principal Component Analysis, Encoding Attributes.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper aims to predict the duration of lawsuits for labor users of the justice system. Thus, we intend to provide forecasts of the duration of a labor lawsuit that gives subsidies to establish an agreement between the parties involved in the processes. The proposed methodology consists in applying and comparing three techniques of the Mathematical Programming area, Artificial Neural Networks (ANN), Multiple Linear Regression (MLR) and Decision Trees in order to obtain the best possible performance for the forecast. Therefore, we used the data from the Labor Forum of São José dos Pinhais, Paraná, Brazil, to do the training of various ANNs, the MLR and the Decision Tree. In several simulations, the techniques were used directly and in others, the Principal Component Analysis (PCA) and / or the coding of attributes were performed before their use in order to further improve their performance. Thus, taking up new data (processes) for which it is necessary to predict the duration of t he lawsuit, it will be possible to make up conditions to "diagnose" its length preliminarily at its course. The three techniques used were effective, showing results consistent with an acceptable margin of error. (More)

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Paper citation in several formats:
Pavanelli, G.; Teresinha Arns Steiner, M.; Memari Pavanelli, A. and Maria Bertholdi Costa, D. (2013). Artificial Neural Networks, Multiple Linear Regression and Decision Trees Applied to Labor Justice. In Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA; ISBN 978-989-8565-77-8; ISSN 2184-3236, SciTePress, pages 443-450. DOI: 10.5220/0004517504430450

@conference{ncta13,
author={Genival Pavanelli. and Maria {Teresinha Arns Steiner}. and Alessandra {Memari Pavanelli}. and Deise {Maria Bertholdi Costa}.},
title={Artificial Neural Networks, Multiple Linear Regression and Decision Trees Applied to Labor Justice},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA},
year={2013},
pages={443-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004517504430450},
isbn={978-989-8565-77-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA
TI - Artificial Neural Networks, Multiple Linear Regression and Decision Trees Applied to Labor Justice
SN - 978-989-8565-77-8
IS - 2184-3236
AU - Pavanelli, G.
AU - Teresinha Arns Steiner, M.
AU - Memari Pavanelli, A.
AU - Maria Bertholdi Costa, D.
PY - 2013
SP - 443
EP - 450
DO - 10.5220/0004517504430450
PB - SciTePress