Reduction of ensemble of classifiers with a rule sets analysis

The article shortly discusses the aim of classification task and its application to different domains of life. The idea of ensemble of classifiers is presented and some aspects of grouping methods are discussed. The paper points to the need of ensemble classifier pruning and presents a new approach for ensemble reduction. The proposed method is dedicated to committees of decision trees and bases on transformation of a tree set into a rule set and the new, suited to the pruning method, the weighted voting algorithm is also presented. There are also described experiments showing properties and effectiveness of the proposed method. Finally, directions of further research are mentioned

ENMTML: An R package for a straightforward construction of complex ecological niche models

Ecological niche models (ENMs) is a popular method in ecology, mostly due to its broad applicability and the fact that required data is simple and easily accessible from digital databases. Nevertheless, there is an underlying methodological complexity, often overlooked by many scientists that rely on ENMs to achieve other objectives. We present here the package ENMTML, an Open Source R package. The main purpose of this package is to assemble all this methodological complexity spread over several papers and bring it into the spotlight in a simple way for people not used to the details of ENMs. The package contains several alternatives to different methodological steps, e.g., pseudo-absence allocation and accessible area delimitation, formulated within a single function, to make it accessible for people not used to the programming environment.Fil: Alves de Andrade, André Felipe. Universidade Federal de Goiás; BrasilFil: Velazco, Santiago José Elías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: De Marco Júnior, Paulo. Universidade Federal de Goiás; Brasi

A Diversity-Accuracy Measure for Homogenous Ensemble Selection

Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy. The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy. This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate. Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods