Feature Subset Selection by Estimation of Distribution Algorithms PDF Download

Author: Erick Cantú-Paz
Publisher:
ISBN:
Category :
Languages : en
Pages : 8

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Book Description
This paper describes the application of four evolutionary algorithms to the selection of feature subsets for classification problems. Besides of a simple genetic algorithm (GA), the paper considers three estimation of distribution algorithms (EDAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the EDAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. All the algorithms found feature subsets that resulted in higher accuracies than using all the features. However, in contrast with other studies, we did not find evidence to support or reject the use of EDAs for this problem.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
This paper describes the application of four evolutionary algorithms to the identification of feature subsets for classification problems. Besides a simple GA, the paper considers three estimation of distribution algorithms (EDAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the EDAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. In contrast with previous studies, we did not find evidence to support or reject the use of EDAs for this problem.

Author: Pedro Larrañaga
Publisher: Springer Science & Business Media
ISBN: 1461515394
Category : Computers
Languages : en
Pages : 398

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Book Description
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.

Author: Kalyanmoy Deb
Publisher: Springer
ISBN: 9783540223443
Category : Computers
Languages : en
Pages : 1448

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Book Description
The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.

Author: Huan Liu
Publisher: CRC Press
ISBN: 1584888792
Category : Business & Economics
Languages : en
Pages : 437

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Book Description
Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the

Author: Kashif Javed Butt
Publisher:
ISBN:
Category :
Languages : en
Pages :

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The main purpose of Feature Subset Selection is to find a reduced subset of attributes from a data set described by a feature set. The task of a feature selection algorithm (FSA) is to provide with a computational solution motivated by a certain defi nition of relevance or by a reliable evaluation measure. Feature weighting is a technique used to approximate the optimal degree of influence of individual features using a training set. When successfully applied relevant features are attributed a high weight value, whereas irrelevant features are given a weight value close to zero. Feature weighting can be used not only to improve classi cation accuracy but also to discard features with weights below a certain threshold value and thereby increase the resource efi ciency of the classifier. In this work several fundamental feature weighting algorithms (FWAs) are studied to assess their performance in a controlled experimental scenario. A measure to evaluate FWAs score is devised that computes the degree of matching between the output given by a FWAs and the known optimal solutions. A study of relation between the score obtained from the di fferent classi fiers, variance of the score in the di fferent sample size is carried out as well as the relation between the score and the estimated probability of error of the model (Pe) for the classification problems and the square error (e2) for the regression problem.

Author: E. Cantu-Paz
Publisher:
ISBN:
Category :
Languages : en
Pages : 14

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Book Description
The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.

Author: Kurosh Madani
Publisher: Springer
ISBN: 3319233920
Category : Technology & Engineering
Languages : en
Pages : 517

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Book Description
The present book includes a set of selected extended papers from the fifth International Joint Conference on Computational Intelligence (IJCCI 2013), held in Vilamoura, Algarve, Portugal, from 20 to 22 September 2013. The conference was composed by three co-located conferences: The International Conference on Evolutionary Computation Theory and Applications (ECTA), the International Conference on Fuzzy Computation Theory and Applications (FCTA), and the International Conference on Neural Computation Theory and Applications (NCTA). Recent progresses in scientific developments and applications in these three areas are reported in this book. IJCCI received 111 submissions, from 30 countries, in all continents. After a double blind paper review performed by the Program Committee, only 24 submissions were accepted as full papers and thus selected for oral presentation, leading to a full paper acceptance ratio of 22%. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of IJCCI 2013. Commitment to high quality standards is a major concern of IJCCI that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.

Author: Gary B. Fogel
Publisher: John Wiley & Sons
ISBN: 0470199083
Category : Computers
Languages : en
Pages : 377

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Book Description
Combining biology, computer science, mathematics, and statistics, the field of bioinformatics has become a hot new discipline with profound impacts on all aspects of biology and industrial application. Now, Computational Intelligence in Bioinformatics offers an introduction to the topic, covering the most relevant and popular CI methods, while also encouraging the implementation of these methods to readers' research.

Author: Huan Liu
Publisher: Springer Science & Business Media
ISBN: 1461557259
Category : Computers
Languages : en
Pages : 418

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Book Description
There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.