Artificial Neural Networks, Multiple Linear Regression and Decision Trees Applied to Labor Justice
Artificial Neural Networks, Multiple Linear Regression and Decision Trees Applied to Labor Justice Genival Pavanelli, Maria Teresinha Arns Steiner, Alessandra Memari Pavanelli and Deise Maria Bertholdi Costa Specialization Program in Numerical Methods in Engennering PPGMNE, Federal University of Paraná State UFPR, CP 1908 , Curitiba, Brazil Keywords: Mathematical Programming, Artificial Neural Networks, Multiple Linear Regression, Decision Tree, Principal Component Analysis, Encoding Attributes. 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 the 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. 1 INTRODUCTION and processing of data.
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