Feature selection for support vector machines with RBF kernel

Liu, Quanzhong; Chen, Chihau; Zhang, Yang; Hu, Zhengguo

doi:10.1007/s10462-011-9205-2

Cited by 80 publications

(45 citation statements)

References 37 publications

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“…where we look at new examples and assign them to classes based on the features we have learned about during training [12]. The task is to introduce a hypothesis (classifier) that accurately predicts the labels of novel instances.…”

Section: Classification (With Assessment) -This Ismentioning

confidence: 99%

Formulation of Feature Selection with Support Vector Machine

Prajapati¹,

Patle²

2015

IJCA

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Basic question arises when classification came in picture classification accuracy, ensemble size, and computational complexity. Feature selection is importance for improvement and performance of classification algorithm. Classification algorithm may not scale up to the size of the full feature set either in sample or time but with feature selection help us to better understand the domain with Cheaper to collect a subset of predictors and Safer to collect a reduced subset of predictors. An important pre-processing step in classification tasks is called as, Feature selection its aims to minimize both the classification error rate and the number of features for inference knowledge in any domain. Feature selection is Minimum set F that achieves maximum classification performance of T (for a given set of classifiers and classification performance metrics). This paper proposes feature selection methodology which includes ranking, information gain and filter method concept. After the feature subset train SVM with RBF kernel for classification.

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Section: Classification (With Assessment) -This Ismentioning

confidence: 99%

Formulation of Feature Selection with Support Vector Machine

Prajapati¹,

Patle²

2015

IJCA

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show abstract

“…For two features that are highly dependent on each other, removing any one of them will not bring about much change in the class discriminative power of the feature. In the study of [23] the dependency between a feature variable x i and the class label y was maximized using Equation (26), while using Equation (27) they minimized the dependency of pair features x i and x j . This constraint is used to filter out only mutually exclusive features.…”

Section: Feature Dimensionality Reductionmentioning

confidence: 99%

“…To produce the classification results, the test feature vectors, however, were tested against all the trained SVM models. The majority voting scheme is finally employed to predict the class of ensemble outputs, using the strategy of the winner takes all [25,26]. In each round of the experiment, 21 unique SVM classifiers are trained to classify the seven basic expressions.…”

Section: Support Vector Machinementioning

confidence: 99%

3D facial expression recognition using maximum relevance minimum redundancy geometrical features

Rabiu

Saripan

Mashohor

et al. 2012

EURASIP J. Adv. Signal Process.

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In recent years, facial expression recognition (FER) has become an attractive research area, which besides the fundamental challenges, it poses, finds application in areas, such as human-computer interaction, clinical psychology, lie detection, pain assessment, and neurology. Generally the approaches to FER consist of three main steps: face detection, feature extraction and expression recognition. The recognition accuracy of FER hinges immensely on the relevance of the selected features in representing the target expressions. In this article, we present a person and gender independent 3D facial expression recognition method, using maximum relevance minimum redundancy geometrical features. The aim is to detect a compact set of features that sufficiently represents the most discriminative features between the target classes. Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise. The average recognition accuracy of 92.2% was recorded. Furthermore, inter database homogeneity was investigated between two independent databases the BU-3DFE and UPM-3DFE the results showed a strong homogeneity between the two databases.

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“…A common drawback of these techniques is that they have a higher risk of overfitting than filter methods and are computationally intensive, especially if building the classifier has a high computational cost [9]. Additional work has been done to assess variable importance in non-linear kernels SVM by modifying SVM-RFE [3, 10, 11]. …”

Section: Introductionmentioning

confidence: 99%

SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

et al. 2018

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BackgroundSupport vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis.ResultsThe proposed algorithms allows visualization of each one the RFE iterations, and hence, identification of the most relevant predictors of the response variable. Using simulation studies based on time-to-event outcomes and three real datasets, we evaluate the three methods, based on pseudo-samples and kernel principal component analysis, and compare them with the original SVM-RFE algorithm for non-linear kernels. The three algorithms we proposed performed generally better than the gold standard RFE for non-linear kernels, when comparing the truly most relevant variables with the variable ranks produced by each algorithm in simulation studies. Generally, the RFE-pseudo-samples outperformed the other three methods, even when variables were assumed to be correlated in all tested scenarios.ConclusionsThe proposed approaches can be implemented with accuracy to select variables and assess direction and strength of associations in analysis of biomedical data using SVM for categorical or time-to-event responses. Conducting variable selection and interpreting direction and strength of associations between predictors and outcomes with the proposed approaches, particularly with the RFE-pseudo-samples approach can be implemented with accuracy when analyzing biomedical data. These approaches, perform better than the classical RFE of Guyon for realistic scenarios about the structure of biomedical data.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2451-4) contains supplementary material, which is available to authorized users.

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Feature selection for support vector machines with RBF kernel

Cited by 80 publications

References 37 publications

Formulation of Feature Selection with Support Vector Machine

Formulation of Feature Selection with Support Vector Machine

3D facial expression recognition using maximum relevance minimum redundancy geometrical features

SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

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