Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

Sarojini, B.; Ramaraj, N.; Nickolas, S.

doi:10.1007/978-3-642-03547-0_51

Cited by 15 publications

(6 citation statements)

References 22 publications

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“…To tackle the first problem, we accelerate the search process by employing the F-score criterion [25,26] to rank the features and using the backward elimination search strategy. For the latter, all the features are first ranked in descending order of the importance of the features.…”

Section: Svm With Feature Selectionmentioning

confidence: 99%

Heartbeat classification using disease-specific feature selection

Zhang

Dong

Luo

et al. 2014

Computers in Biology and Medicine

308

169

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Section: Svm With Feature Selectionmentioning

confidence: 99%

Heartbeat classification using disease-specific feature selection

Zhang

Dong

Luo

et al. 2014

Computers in Biology and Medicine

308

169

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“…After cross validation and grid search to find the optimal parameters, the prediction was tested by using a test feature vector of size 570 × 24. The average accuracy, which was computed by taking into account both false negatives and false positives as proposed in [ 46 ], resulted to be equal to 88.2%. Table 3 summarizes the results of classification on each individual volunteer.…”

Section: Resultsmentioning

confidence: 99%

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

2010

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BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.

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“…To identify the most important descriptors of the molecules for making these predictions, an independent feature selection analysis was required since the importance of each feature cannot be computed explicitly from a nonlinear SVM. 36 Several feature selection methods have previously been applied to problems to improve classifier performance, including the F-score statistical test 37 which identifies important features, and recursive feature elimination 36 which removes redundant features. We compared the accuracy of many classifiers built using only a single molecular descriptor 38 in order to find the most important feature.…”

Section: Machine Learningmentioning

confidence: 99%