Abstract The objective of this work was to apply the random forest (RF) algorithm modelling aboveground carbon (AGC) stock a tropical by testing three feature selection procedures – recursive removal and uniobjective multiobjective genetic algorithms (GAs). used database covered 1,007 plots sampled in Rio Grande watershed, state Minas Gerais state, Brazil, 114 environmental variables (climatic,...

Nordhausen and Langley’s IDS is a system for automated integrated scientific discovery. IDS’ is-a hierarchy both organizes knowledge and constrains search. This search bias, however, limits what may be discovered to knowledge learnable by operators that execute local tree manipulations on the is-a hierarchy. I present an alternative approach which uses representation reduction as the driving bi...

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature s...

Laboratory of Computational Intelligence, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Av. Trabalhador São-carlense, 400, 13566-590 São Carlos, SP, Brazil Laboratory of Bioinformatics, Centro de Engenharias e Ciências Exatas, Universidade Estadual do Oeste do Paraná, Av. Tarqúınio Joslin dos Santos, 1300, 85867-900 Foz do Iguaçu, PR, Brazil Coloproctology Servic...

We present an efficient feature selection method that can find all multiplicative combinations of continuous features that are statistically significantly associated with the class variable, while rigorously correcting for multiple testing. The key to overcome the combinatorial explosion in the number of candidates is to derive a lower bound on the p-value for each feature combination, which en...

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each featu...