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KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems ⋆
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C.: Compare: classification of morphological patterns using adaptive regional elements
- IEEE Transaction on Medical Imaging
, 2007
"... Abstract—This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass ..."
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Cited by 63 (14 self)
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Abstract—This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8 % for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used. Index Terms—Feature selection, morphological pattern analysis, pattern classification, structural MRI, regional feature extraction, schizophrenia, support vector machines (SVM). I.
Normalized mutual information feature selection
- IEEE TRANS. NEURAL NETW.
, 2009
"... A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti’s MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy amo ..."
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Cited by 31 (1 self)
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A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti’s MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy among features. NMIFS outperformed MIFS, MIFS-U, and mRMR on several artificial and benchmark data sets without requiring a user-defined parameter. In addition, NMIFS is combined with a genetic algorithm to form a hybrid filter/wrapper method called GAMIFS. This includes an initialization procedure and a mutation operator based on NMIFS to speed up the convergence of the genetic algorithm. GAMIFS overcomes the limitations of incremental search algorithms that are unable to find dependencies between groups of features.
K.L.: Parallel evolutionary algorithms on graphics processing unit
- In: Proceedings of IEEE Congress on Evolutionary Computation 2005 (CEC 2005). Volume
, 2005
"... Abstract- Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be p ..."
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Cited by 14 (1 self)
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Abstract- Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose to implement a parallel EA on consumer-level graphics cards. We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications. 1
Feature Selection using PSO-SVM
"... Abstract—The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Feature selection is of great importa ..."
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Cited by 11 (0 self)
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Abstract—The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Feature selection is of great importance in pattern classification, medical data processing, machine learning, and data mining applications. Therefore, a good feature selection method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this paper, particle swarm optimization (PSO) is used to implement a feature selection, and support vector machines (SVMs) with the one-versus-rest method serve as a fitness function of PSO for the classification problem. The proposed method is applied to five classification problems from the literature. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to the other feature selection methods.
Bayes classification of online arabic characters by gibbs modeling of class conditional densities
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu et al. constrainedmaxim ..."
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Cited by 11 (1 self)
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Abstract—This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu et al. constrainedmaximumentropy formalism, originally introduced for image and shape synthesis. We investigate two partition function estimation methods: one uses the training sample, and the other draws from a reference distribution. The efficiency of the corresponding Bayes decision methods, and of a combination of these, is shown in experiments using a database of 9,504 freely written samples by 22 writers. Comparisons to the nearest neighbor rule method and a Kohonen neural network method are provided. Index Terms—Bayes classification, Gibbs density parameter estimation, histograms, online handwritten Arabic character recognition. Ç 1
Feature Subset Selection using Ant Colony Optimization
- International Journal of Computational Intelligence
, 2006
"... Abstract—Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has b ..."
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Cited by 9 (2 self)
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Abstract—Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results. Keywords—Ant Colony Optimization, ant systems, feature selection, pattern recognition.
Selecting discrete and continuous features based on neighborhood decision error minimization
- IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics
, 2010
"... Abstract—Feature selection plays an important role in pattern recognition and machine learning. Feature evaluation and classifi-cation complexity estimation arise as key issues in the construction of selection algorithms. To estimate classification complexity in different feature subspaces, a novel ..."
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Cited by 8 (1 self)
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Abstract—Feature selection plays an important role in pattern recognition and machine learning. Feature evaluation and classifi-cation complexity estimation arise as key issues in the construction of selection algorithms. To estimate classification complexity in different feature subspaces, a novel feature evaluation measure, called the neighborhood decision error rate (NDER), is proposed, which is applicable to both categorical and numerical features. We first introduce a neighborhood rough-set model to divide the sample set into decision positive regions and decision boundary regions. Then, the samples that fall within decision boundary regions are further grouped into recognizable and misclassified subsets based on class probabilities that occur in neighborhoods. The percentage of misclassified samples is viewed as the estimate of classification complexity of the corresponding feature subspaces. We present a forward greedy strategy for searching the feature subset, which minimizes the NDER and, correspondingly, mini-mizes the classification complexity of the selected feature subset. Both theoretical and experimental comparison with other feature selection algorithms shows that the proposed algorithm is effective for discrete and continuous features, as well as their mixture. Index Terms—Continuous feature, decision error minimization, discrete feature, feature selection, neighborhood, rough sets. I.
Improving writer identification by means of feature selection and extraction
- In Proc. ICDAR 2005
, 2005
"... To identify the author of a sample handwriting from a set of writers, 100 features are extracted from the handwriting sample. By applying feature selection and extraction methods on this set of features, subsets of lower dimensionality are obtained. We show that we can achieve significantly better w ..."
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Cited by 8 (1 self)
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To identify the author of a sample handwriting from a set of writers, 100 features are extracted from the handwriting sample. By applying feature selection and extraction methods on this set of features, subsets of lower dimensionality are obtained. We show that we can achieve significantly better writer identification rates if we use smaller feature subsets returned by different feature extraction and selection methods. The methods considered in this paper are feature set search algorithms, genetic algorithms, principal component analysis, and multiple discriminant analysis.
