A Novel Feature Selection Method for Process Fault Diagnosis

2013 ◽  
Vol 427-429 ◽  
pp. 2045-2049
Author(s):  
Chun Mei Yu ◽  
Sheng Bo Yang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Selection Process ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Fault Classification ◽  
Process Data ◽  
Bhattacharyya Distance

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

A Novel Feature Selection Method Based on Principal Component Analysis for Network Traffic Classification

2014 ◽  
Vol 989-994 ◽  
pp. 4510-4513
Author(s):  
Hong Zhi Wang ◽  
Jian Ping Zhang ◽  
Zun Yi Shang
Keyword(s):  
Feature Selection ◽  
Network Traffic ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Performance Comparison ◽  
Traffic Classification ◽  
Network Traffic Classification ◽  
Validation Experiment ◽  
Pca Method

In network traffic classification, by conventional PCA method, more features still exist due to uniform contribution rates for most of features. To overcome this problem, in this paper, a novel feature selection method is proposed to reduce data dimension of network traffic. A contribution rate of various features in each component is calculated by a new weight criterion. A maxima-order principle is proposed to determine feature selection. Based on three multi-class classification methods, performance comparison is conducted by actual traffic data with 10-fold cross-validation. Experiment shows that the proposed method has higher classification accuracy than conventional PCA method.

scholarly journals Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method

2021 ◽  
Vol 2129 (1) ◽  
pp. 012022
Author(s):  
Mohamad Faiz Dzulkalnine ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Nor Haizan Mohd Radzi ◽  
Noorfa Haszlinna Binti Mustaffa ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Principal Component ◽  
Selection Method ◽  
Cancer Dataset ◽  
Medical Practitioners ◽  
Fuzzy Feature Selection ◽  
Fuzzy Principal Component Analysis ◽  
Learning Analysis

Abstract In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.

scholarly journals Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection

2018 ◽  
Vol 8 (11) ◽  
pp. 2143 ◽  
Author(s):  
Xianghong Tang ◽  
Jiachen Wang ◽  
Jianguang Lu ◽  
Guokai Liu ◽  
Jiadui Chen
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Feature Selection Method ◽  
Selection Method ◽  
Diagnostic Feature ◽  
Feature Subset ◽  
Maximum Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Coefficient

Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between features and fault categories by exploiting the nonlinearity capturing capability of maximum information coefficient. In this method, a weak correlation feature subset obtained by a Feature-to-Feature-Maximum Information Coefficient (FF-MIC) matrix and a strong correlation feature subset obtained by a Feature-to-Category-Maximum Information Coefficient (FC-MIC) matrix are merged into a final diagnostic feature set by an intersection operation. To evaluate the proposed FF-FC-MIC method, vibration data collected from two bearing fault experiment platforms (CWRU dataset and CUT-2 dataset) were employed. Experimental results showed that accuracy of FF-FC-MIC can achieve 97.50%, and 98.75% on the CWRU dataset at the motor speeds of 1750 rpm, and 1772 rpm, respectively, and reach 91.75%, 94.69%, and 99.07% on CUT-2 dataset at the motor speeds of 2000 rpm, 2500 rpm, 3000 rpm, respectively. A significant improvement of FF-FC-MIC has been confirmed, since the p-values between FF-FC-MIC and the other methods are 1.166 × 10 − 3 , 2.509 × 10 − 5 , and 3.576 × 10 − 2 , respectively. Through comparison with other methods, FF-FC-MIC not only exceeds each of the baseline feature selection method in diagnosis accuracy, but also reduces the number of features.

A Hierarchical Feature Selection Method Based on Classification Tree for HGU Fault Diagnosis

2014 ◽  
Vol 1037 ◽  
pp. 398-403 ◽  
Author(s):  
Xiao Yue Chen ◽  
Jian Zhong Zhou ◽  
Xiao Min Xu ◽  
Yong Chuan Zhang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Heuristic Search ◽  
Classification Tree ◽  
Evaluation Criteria ◽  
Feature Selection Method ◽  
Selection Method ◽  
Safe Operation ◽  
Feature Selection Technique ◽  
Hydraulic Generator

Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Because of the complexity of HGU, the vast amounts of measured data and the redundant information, the accuracy and instantaneity of fault diagnosis are severely limited. At present, feature selection technique is an effective method to break through this bottleneck. According to the specific characteristics of HGU faults, this paper puts forward a hierarchical feature selection method based on classification tree (HFSMCT). HFSMCT selects the most effective feature for each branch node through filtering evaluation criteria and heuristic search strategy, and all the selected features constitute the final feature set. Moreover, HFSMCT is easy to design and implement, and it is very prominent in computational efficiency and accuracy. The simulation results also prove that HFSMCT is very suitable for HGU fault diagnosis.

scholarly journals Unsupervised Optimal Discriminant Vector Based Feature Selection Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Su-Qun Cao ◽  
Jonathan H. Manton
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Objective Function ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Subset ◽  
Fisher Criterion ◽  
Feature Importance ◽  
Discriminant Vector ◽  
Class Labels

An efficient unsupervised feature selection method based on unsupervised optimal discriminant vector is developed to find the important features without using class labels. Features are ranked according to the feature importance measurement based on unsupervised optimal discriminant vector in the following steps. First, fuzzy Fisher criterion is adopted as objective function to derive the optimal discriminant vector in unsupervised pattern. Second, the feature importance measurement based on elements of unsupervised optimal discriminant vector is defined to determine the importance of each feature. The features with little importance measurement are removed from the feature subset. Experiments on UCI dataset and fault diagnosis are carried out to show that the proposed method is very efficient and able to deliver reliable results.

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