Relevance and Redundancy Analysis for Ensemble Classifiers

Duangsoithong, Rakkrit; Windeatt, Terry

doi:10.1007/978-3-642-03070-3_16

Cited by 21 publications

(27 citation statements)

References 13 publications

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“…The features are checked in pairs using redundancy analysis method [23,24]. Firstly, two sets of feature vectors, 1 and 2 , are selected randomly from the feature set obtained by ReliefF.…”

Section: Feature Integration Methods Based On Compressed Randommentioning

confidence: 99%

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Ding

Wang

et al. 2018

Complexity

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Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR) method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

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“…The features are checked in pairs using redundancy analysis method [23,24]. Firstly, two sets of feature vectors, 1 and 2 , are selected randomly from the feature set obtained by ReliefF.…”

Section: Feature Integration Methods Based On Compressed Randommentioning

confidence: 99%

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Ding

Wang

et al. 2018

Complexity

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“…If the SU between the feature and the class equals 1, this feature is definitely related to that class. However, if the SU is equal to 0, the features are believed to be irrelevant to this class [35].…”

Section: Data Set Relevance Analysismentioning

confidence: 99%

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Şen¹,

Peker²

2013

Turk J Elec Eng & Comp Sci

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This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms.In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

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“…Duangsoithong and Windeatt [10] presented a method for reducing the data volume in the series which have high features and low samples as well as classification performance. By removing the irrelevant and repetitive data, new data sets are created.…”

Section: Introductionmentioning

confidence: 99%

Application Of Ant Colony Algorithm And Principal Components Analysis In The Diagnosis Of Lung Cancer

Ayat

Rahi²

2014

J. Math. Computer Sci.

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This paper presents a new method for diagnosing lung cancer by combination of ant colony algorithm, fuzzy logic and principal component analysis (PCA). In this method, PCA method is used to reduce the size of data sets, the fuzzy logic is used to create fuzzy rules that make it possible to be interpreted by experts. Finally, these fuzzy rules are optimized by ant colony algorithm (ACO). Evaluation and comparing the proposed method with other methods have been proposed to implement this approach, leading to lung cancer dataset with criteria such as speed, reliability, and the ability to interpret the show.

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Relevance and Redundancy Analysis for Ensemble Classifiers

Cited by 21 publications

References 13 publications

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Application Of Ant Colony Algorithm And Principal Components Analysis In The Diagnosis Of Lung Cancer

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