Search

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems. [ABSTRACT FROM AUTHOR]

Facial paralysis (FP) is the most common illness. Nerve damage can cause the affected muscles of the face to lose control. Most FP diagnosis systems heavily depend on skilled clinicians and lack automatic quantitative assessment. This paper introduces a novel automatic facial paralysis recognition (autoFPR) approach, a four-stage machine learning solution, for classifying FP and healthy subjects. Our solution includes the creation of the dataset, feature extraction, dimensionality reduction, and classification. The FP and healthy subject videos are collected from the publicly available YouTube Facial Palsy database and the 300VW database, respectively. Facial features are extracted from the collected facial videos using landmarks, action units, eye gaze, and head pose. Five different experiments were performed to find the best facial paralysis recognition technique. autoFPR can distinguish subjects with facial paralysis and healthy individuals by using movement changes in their eyebrows, eyes, and corners of the mouth. Our goal is to prove that machine learning classifiers are capable of diagnosing FP. We evaluate system performance using accuracy, precision, recall, and F1. Feature selection plus principal component analysis with support vector machine achieved the highest accuracy, with 97.3% for the extracted features. [ABSTRACT FROM AUTHOR]

Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing GT based FS techniques are restricted to Zero Sum Games, Non-Zero Sum Games and Cooperative Games. The classical setting of assuming that all the details of players are known to all players cannot hold in many real-world problems. When the given features are independent, they cannot be treated alike and a characteristics based uncertainty persists among the features. This uncertainty is handled by none of the game forms used in the existing methods. Unlike the mentioned game techniques, Bayesian Games (BG) address the games with imperfect information. This paper investigate the FS problem in terms of BG and proposes a novel method to select the best features. The proposed BG based FS method is a filter type FS method and it starts with identifying Principle Features (PF) and proceeds to play global pairwise Bayesian games between those PF to obtain feature scores. Later, the features are ranked using these scores. In the final stage, a forward selection method with Support Vector Machine (SVM) is used to evaluate the classification performance of the ranked features and helps in the selection of the optimal set of features. Besides these, to improve the scalability of the proposed method MapReduce paradigm is exploited. In order to show the efficacy of the proposed method, experiments are carried out with seven real-world datasets from UCI and Statlog repositories. The promising results showed a significant improvement in the classification performance with fewer selected features than which is achieved using the existing methods. [ABSTRACT FROM AUTHOR]

Software Defect Prediction (SDP) involves the early detection of fault-prone modules and reduces the testing efforts and cost. Support Vector Machine (SVM)-based SDP classifiers use large amount of high-dimensional data, and hence feature selection (FS) is applied for better accuracy. Search-based feature selection is found effective to improve the efficiency of predictors. This paper proposes a genetic evolution (GeEv) technique to select features. The GeEv technique involves introduction of diversity at intermediate level by the genetic evolution of random offspring with better survival capability. GeEv searches the feature space for an optimal feature subset using the performance of classification and number of features selected as the fitness function. The FS is modeled as an optimization problem and optimal solution is sought using GeEv. The performance is compared with baseline technique. From the experimental results, it is shown GeEv outperforms the competing FS approach and can achieve better accuracy than others statistically. [ABSTRACT FROM AUTHOR]

Feature selection is a preprocessing step that plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effective in removing redundant and irrelevant features, improving the learning algorithm's prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we developed a novel extension of FCBF, called resampling FCBF (RFCBF) that combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using three competitive classifiers (K-nearest neighbor, support vector machine, and logistic regression) on 12 publicly available datasets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime. [ABSTRACT FROM AUTHOR]

This paper analyzes a new regularized learning scheme for high-dimensional partially linear support vector machine (SVM). The proposed approach consists of an empirical risk and the Lasso-type penalty for linear part, as well as the standard functional norm for nonlinear part. Here, the linear kernel is used for model interpretation and feature selection, while the nonlinear kernel is adopted to enhance algorithmic flexibility. In this paper, we develop a new technical analysis on the weighted empirical process, and establish the sharp learning rates for the semi-parametric estimator under the regularized conditions. Specially, our derived learning rates for semi-parametric SVM depend on not only the sample size and the functional complexity, but also the sparsity and the margin parameters. [ABSTRACT FROM AUTHOR]

As an art image, Thangka images have rich themes, various forms of expression, complex picture content and many layers of color representation. This paper mainly constructs a multi-core support vector machine (SVM) based on the information entropy feature-weighted radial basis kernel function. In this paper, the kernel function is optimized, and the feature reduction is performed by using the random forest feature selection algorithm with average accuracy degradation. Finally, the effective classification of the icon image and the mandala image in Thangka is realized. The research results provide support for the follow-up study of Thangka image annotation and retrieval. [ABSTRACT FROM AUTHOR]

This paper presents a novel model for ultrasonic defect identification relying on multi-feature fusion and multi-criteria feature evaluation (MFF-MCFE). Based on feature extraction, feature selection, pattern recognition and data fusion algorithm, this model analyzes ultrasonic echo signal data from single-probe ultrasonic inspection, and based on wavelet packet transform (WPT), empirical mode decomposition (EMD) and discrete wavelet transform (DWT), the main features from the collected ultrasonic echo signals are also extracted. These features are also evaluated by means of Representation Entropy (RE), Fisher's ratio (FR) and Mahalanobis distance (MD), and the results are fused with Dempster–Shafer (D-S) evidence theory and the corresponding feature subsets are formed according to the fusion result. The support vector machine (SVM) is used as the classifier to recognize the defect signal, and the subsequent classification results are integrated by D-S evidence theory, which leads to the final recognition results. On this basis, a series of experiments were carried out to compare the performance of the developed model with that of the models using single feature sets and single feature evaluation criterion. Meanwhile, the principal component analysis (PCA) was also involved in the corresponding comparative analysis. The experimental results showed that this model is suitable for the identification and diagnosis of pipeline defects, and its classification accuracy could be reached up to 96.29% with stronger robustness and stability. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach for classification of faults in Extra High Voltage (EHV) transmission line with series compensation. The proposed algorithm utilizes single end currents extracted from three phases of a transmission line given to a fault detection and classification system. A detailed model is constructed to analyze fault patterns occurring on a dual feed system with multiple series compensation provided on EHV transmission line. The algorithm uses a full cycle of post-fault currents. A Multiresolution Analysis Wavelet-based decomposition technique is used to provide a joint time frequency analysis. Extensive study is done by designing different fault classifiers using Support Vector Machine (SVM) with different feature vector groups. The SVMs with different parameters are compared to show performance of SVMs in given feature space of faults. A new algorithm is proposed with Ensemble method having group of different classifiers, namely, Artificial Neural Network (ANN), K-Nearest Neighborhood (KNN) and SVM. A combination of feature selection with Ensemble Classifiers is trained and tested on a wide range of fault patterns. A significant improvement in performance is obtained with different combination of features in Ensemble Classifier using subspace partition method.The proposed Ensemble Classifier provides an accuracy of 99.5%. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coefficient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naïve Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively. [ABSTRACT FROM AUTHOR]

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM). [ABSTRACT FROM AUTHOR]

Mastitis is the major cause of loss in dairy farming. Somatic cells are one of most important standards to detect this infection. This paper proposes a novel image processing algorithm to recognize four types of somatic cells in bovine milk automatically. First, cloud model uses to segment cell images. Second, a variety of features are extracted from regions of interest. Finally, most differential features are selected using ReliefF algorithm and performances of two classifiers, Back propagation networks (BPN) and support vector machine (SVM), are compared. The experimental results are obtained using a large set of images from different sources. The results of our proposed method is not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy. [ABSTRACT FROM AUTHOR]

As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine. [ABSTRACT FROM AUTHOR]

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms. [ABSTRACT FROM AUTHOR]

Paraphrase Recognition systems most often use various lexical, syntactic and semantic features to recognize paraphrases. This paper presents the work done in designing a Support Vector Machine (SVM) based Paraphrase Recognizer and then improving its performance using feature selection strategy. Wrapper method of feature selection has been adopted by combining Genetic Algorithms with Support Vector Machine Classifiers. Experimental results show that applying Feature selection improves the accuracy besides reducing the number of features. The developed paraphrase recognizer has been applied for the Student Answer Evaluation task. The results obtained show that the performance of Answer Evaluation systems which use only half the number of features is comparable to systems using the original feature set. [ABSTRACT FROM AUTHOR]

A preprocessing stage in every speech/music applications including audio/speech separation, speech/speaker recognition and audio/genre transcription task is inevitable. The importance of such pre-processing stage is originated from the requisite of determining each frame of the given signal is belonged to which classes, namely: speech only, music only or speech/music mixture. Such classification can significantly decrease the computational burden due to exhaustive search commonly introduced as a problem in model-based speech recognition or separation as well as music transcription scenarios. In this paper, we present a new method to separate mixed type audio frames based on support vector machine (SVM) and neural network. We present a feature type selection algorithm which seeks for the most appropriate features to discriminate possible classes (hypotheses) on the mixed signal. We also propose features based on eigen-decomposition on the mixed frame. Experimental results demonstrate that the proposed features together with the selected audio classifiers achieve acceptable classification results. From the experimental results, it is observed that the proposed system outperforms other classification systems including k-nearest neighbor (k-NN) and multi-layer perceptron (MLP). [ABSTRACT FROM AUTHOR]

The early identification of mild cognitive impairment (MCI) has the potential for timely therapeutic interventions that would limit the advancement of MCI to Alzheimer's disease (AD). This paper presents an automated approach for early detection of MCI through pattern classification of magnetic resonance imaging (MRI) data. The approach is based on image feature selection and support vector machine (SVM) classification. Subjects were selected from the Open Access Series of Imaging Studies (OASIS) database and included 89 MCI subjects and 80 controls. Voxel-by-voxel differences in gray matter (GM) intensity between the MCI and control groups were identified. Then regions of interest (ROIs) and the most discriminative image features that represented the patterns in MCI subjects were determined for training a classifier. The classifier demonstrated a high classification accuracy (90%) when a behavioral estimate of MCI and the ROIs were included as features in comparison to the behavioral estimate or the ROIs alone, which is one scientific contribution of our work. Another contribution is that the classifier can be integrated with the image processing functions through an online interface with significant medical capability that can be used for automated image pre-processing, obtaining MCI probability estimates for individual cases, and visualization of affected regions. [ABSTRACT FROM AUTHOR]

Physiological sensors have been used to detect different stress levels in order to improve human health and well-being. When analyzing these sensor data, sensor features are generated in the experiment and a subset of the features are selected and then combined using a host of informatics techniques (machine learning, data mining, or information fusion). Our previous work studied feature selection using correlation and diversity as well as feature combination using five methods C4.5, Naive Bayes, Linear Discriminant Function, Support Vector Machine, and k-Nearest Neighbors. In this paper, we use combinatorial fusion, based on performance criterion (CF-P) and cognitive diversity (CF-CD), to combine those multiple sensor features. Our results showed that: (a) sensor feature combination method is distinctly much better than CF-CD and other algorithms, and (b) CF-CD is as good as other five feature combination methods, and is better in most of the cases. [ABSTRACT FROM AUTHOR]

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors. [ABSTRACT FROM AUTHOR]

Feature selection has been extensively applied in statistical pattern recognition as a mechanism for cleaning up the set of features that are used to represent data and as a way of improving the performance of classifiers. Four schemes commonly used for feature selection are Exponential Searches, Stochastic Searches, Sequential Searches, and Best Individual Features. The most popular scheme used in text categorization is Best Individual Features as the extremely high dimensionality of text feature spaces render the other three feature selection schemes time prohibitive. This paper proposes five new metrics for selecting Best Individual Features for use in text categorization. Their effectiveness have been empirically tested on two well- known data collections, Reuters-21578 and 20 Newsgroups. Experimental results show that the performance of two of the five new metrics, Bayesian Rule and F-one Value, is not significantly below that of a good traditional text categorization selection metric, Document Frequency. The performance of another two of these five new metrics, Low Loss Dimensionality Reduction and Relative Frequency Difference, is equal to or better than that of conventional good feature selection metrics such as Mutual Information and Chi-square Statistic. [ABSTRACT FROM AUTHOR]

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection. [ABSTRACT FROM AUTHOR]