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Feature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in ord...

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the dif...

DNA Microarrays are powerful tools to analyze and identify certain disease from the expression level of the genes in tissues samples. Many machine learning techniques are suitable for building predictive models to classi...

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include t...

Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensi...

Subjects / keywords: Feature selection; Artificial intelligence; Selection (genetic algorithm); Computer science; Feature (linguistics); Pattern recognition (psychology); Machine learning; Linguistics; Philosophy

Though several generations of critics have reported finding English influences in the poetry of Luis Cernuda, no detailed or systematic study of the question has yet been undertaken. This book tackles the issue, concentr...

No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and c...

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. R...

PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural N...

MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links betwee...

Evolutionary algorithms (EAs) can be used as optimisation mechanisms. Based on the model of natural evolution, they work on populations of individuals instead of on single solutions. In this way, the search is performed ...

In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individual...

The performance of two selection mechanisms used in the most popular variant of differential evolution, known as DE/rand/1/bin, are compared in the solution of constrained numerical optimization problems. Four performanc...

In evolutionary algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good indiv...

The Origin of Species, by Charles Darwin, is part of the Barnes & Noble Classicsseries, which offers quality editions at affordable prices to the student and the general reader, including new scholarship, thoughtful desi...

Collinearity refers to the non independence of predictor variables, usually in a regression‐type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation bec...

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most in...

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this...

It is suggested that evolution in plants may be associated with the emergence of three primary strategies, each of which may be identified by reference to a number of characteristics including morphological features, res...

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time...

An adaptationist programme has dominated evolutionary thought in England and the United States during the past 40 years. It is based on faith in the power of natural selection as an optimizing agent. It proceeds by break...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

In spite of the NP-completeness of the satisfiability decision problem (SAT problem), many researchers have been attracted by it because SAT has many applications in Artificial Intelligence. This paper presents a randomi...