Resumen

CEPERO PEREZ, Nayma; MORENO ESPINO, Mailyn; GARCIA BORROTO, Milton  y  MORALES, Eduardo F.. Proactive Forest: Analysis of the impact of the generalization of the diversity parameter. RCCI [online]. 2023, vol.17, n.1, pp. 45-59.  Epub 01-Ene-2024. ISSN 2227-1899.

The ease of interpreting the predictions made by a learned model is one of the advantages that make decision trees one of the most effective techniques when facing a data-mining task. The predictions made by many decision trees can be combined in order to improve the final decision, from this idea arises the concept of decision forests. It is a necessary condition for building a decision forest that the individual trees have a high predictive power and at the same time are different from each other. This difference is known as decision forest diversity, and achieving it is not a trivial process. The most commonly used decision forest algorithms use randomization in the process of constructing each tree to obtain diversity; however, the use of randomization does not always guarantee obtaining adequate diversity. Proactive Forest is a decision forest construction algorithm that introduces a randomness control mechanism based on the definition of an update function of the probabilities with which the attributes are used, one of the most important elements is the diversity parameter that was initially defined as 0.1. The objective of this work is to analyze the use of a single value of the diversity parameter for all the databases. The results show that it is not correct to generalize a diversity value, since the effectiveness is affected depending on the value used.

Palabras clave : accuracy; diversity; decision forest; Proactive Forest; diversity parameter.

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