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In this paper, we study neural network ensembles (NNE) classifier with regularized negative correlation learning (RNCL) and its application to pattern classification. In RNCL algorithm, the regularization parameter is used to control the trade off between mean square error and regularization, and to improve the ensemble's generalization ability. We propose an automatic RNCL algorithm based on gradient...
In general, music retrieval and classification methods using music moods use a lot of acoustic features similar to music genre classification. These features are used as the spectral features, the rhythm features, the harmony features, and so on. However, all of these features may not be efficient for music retrieval and classification using music moods. Hence, in this paper, we propose a feature...
In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods...
A fundamental problem in machine learning is to discriminate a representative set of features on which to construct a classification model for a particular task. This paper presents a feature selection algorithm RF-MI for multiple classes based on ReliefF algorithm and Mutual Information (MI) measure. RF-MI algorithm gets a feature subset by excluding irrelevant and redundant features from original...
Protein features are often complex, and they are challenging to classify. In identifying the most discriminatory features in protein sequences, we propose a new feature-selection strategy by integrating the multivariate filter and Particle Swarm Optimisation (PSO) algorithms. Experimental results, based on the number of reducts and classification accuracy, were analysed in both the filter and wrapper...
Micro array data have a low instance-count and high dimensionality problem which prevent classifiers from building accurate models. This may result in significantly different classification accuracies across classifiers and features chosen. Therefore it is important to select the classifier and feature selection method that perform well on a specific data set. This paper proposes a novel criterion...
The ever growing presence of data lead to a large number of proposed algorithms for classification and especially decision trees over the last few years. However, learning decision trees from large irrelevant datasets is quite different from learning small and moderate sized datasets. In practice, use of only small and moderate sized datasets is rare. Unfortunately, the most popular heuristic function...
In an earlier paper, we proposed a new negative correlation learning (NCL) algorithm for classification ensembles, called AdaBoost.NC, which has significantly better performance than the standard AdaBoost and other NCL algorithms on many benchmark data sets with low computation cost. In this paper, we give deeper insight into this algorithm from both theoretical and experimental aspects to understand...
Relational database mining, where data are mined across multiple relations, is increasingly commonplace. When considering a complex database schema, it becomes difficult to identify all possible relationships between attributes from the different relations. That is, seemingly harmless attributes may be linked to confidential information, leading to data leaks when building a model. In this way, we...
Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence,...
Regarding to the disadvantage of Naive Bayesian Classifier (NBC), this paper proposes a new weighted Naive Bayesian Classifier model, which is based on information gain theory (IGWNBC). Using information gain of attribute in attribute set in sample space, we can reduce attribute set, and assign relative weight to each classification attribute. And the result of it is that strengthens attributes, which...
This paper analyzes the concentration and dispersion of the integrated feature selection algorithm (TFFS),and finds their shortcomings: it is difficult for concentration to measure the weigh of the low frequent terms; dispersion ignores the impact of term whose mutual information is negative. Propose a modified feature selection algorithm (TFFSL), which makes certain improvements on concentration...
Many brain events and disorders can be detected by analyzing electroencephalograms (EEGs). Also the availability of quantitative biological markers that are correlated with qualitative psychiatric phenotypes helps us to utilize automated methods to diagnose and classify these phenotypes. One such a psychiatric phenotype is alcoholism. In this study a method to select an optimal subset of EEG channels...
Multivariate pattern classification is emerging as a powerful tool for analysis of fMRI group studies and has the advantage that it utilizes spatial correlation between brain voxels. However, this makes quantifying the information content of brain voxels and localizing informative brain regions difficult. In this paper we a probabilistic Gaussian process classifiers to compute a sensitive measure...
This paper presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, feature selection is translated into the model selection of SVM which has been well studied. In more detail, the basic idea of this method is to tune the parameters of the Gaussian ARD (Automatic Relevance Determination) kernel via optimization of kernel...
In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve (AUC) has been widely utilized to assess the performance of the classifiers in such cases. The Maximum AUC Linear Classifier (MALC), aiming at maximizing AUC directly, is a nonparametric...
This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.
This paper addresses the problem of improving the accuracy of character recognition with a limited quantity of data. The key ideas are twofold. One is distortion-tolerant template matching via hierarchical global/partial affine transformation (GAT/PAT) correlation to absorb both linear and nonlinear distortions in a parametric manner. The other is use of multiple templates per category obtained by...
In the design of Classifier Ensembles, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to use feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the use of a simple reinforcement-based...
In the context of ensemble systems, feature selection methods can be used to provide different subsets of attributes for the individual classifiers, aiming to reduce redundancy among the attributes of a pattern and to increase the diversity in such systems. Among the several techniques that have been proposed in the literature, optimization methods have been used to find the optimal subset of attributes...
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