Predicting Carbonylation Sites of Human Proteins with a New Max-Significance and Min-Redundancy Feature Selection Criterion

2018 ◽  
Author(s):  
Hongqiang Lyu ◽  
Lele Hao ◽  
Jiguang Zheng ◽  
Congyi Liu ◽  
Yongyi Liu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Selection Criterion ◽  
Human Proteins

Fault Diagnosis of Roller Bearing Conditions Using ANFIS

2009 ◽  
Vol 16-19 ◽  
pp. 886-890 ◽  
Author(s):  
Wen Tao Sui ◽  
Dan Zhang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Roller Bearing ◽  
Back Propagation ◽  
Selection Criterion ◽  
Data Set ◽  
Inference System ◽  
Roller Bearings

This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

Mahalanobis-ANOVA criterion for optimum feature subset selection in multi-class planetary gear fault diagnosis

2021 ◽  
pp. 107754632110291
Author(s):  
Setti Suresh ◽  
VPS Naidu
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Selection Criterion ◽  
Feature Subset Selection ◽  
Fault Classification ◽  
Support Vector ◽  
Feature Subset ◽  
Gear Fault

The empirical analysis of a typical gear fault diagnosis of five different classes has been studied in this article. The analysis was used to develop novel feature selection criteria that provide an optimum feature subset over feature ranking genetic algorithms for improving the planetary gear fault classification accuracy. We have considered traditional approach in the fault diagnosis, where the raw vibration signal was divided into fixed-length epochs, and statistical time-domain features have been extracted from the segmented signal to represent the data in a compact discriminative form. Scale-invariant Mahalanobis distance–based feature selection using ANOVA statistic test was used as a feature selection criterion to find out the optimum feature subset. The Support Vector Machine Multi-Class machine learning algorithm was used as a classification technique to diagnose the gear faults. It has been observed that the highest gear fault classification accuracy of 99.89% (load case) was achieved by using the proposed Mahalanobis-ANOVA Criterion for optimum feature subset selection followed by Support Vector Machine Multi-Class algorithm. It is also noted that the developed feature selection criterion is a data-driven model which will contemplate all the nonlinearity in a signal. The fault diagnosis consistency of the proposed Support Vector Machine Multi-Class learning algorithm was ensured through 100 Monte Carlo runs, and the diagnostic ability of the classifier has been represented using confusion matrix and receiver operating characteristics.

scholarly journals An Occlusion-Robust Feature Selection Framework in Pedestrian Detection †

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2272 ◽  
Author(s):  
Zhixin Guo ◽  
Wenzhi Liao ◽  
Yifan Xiao ◽  
Peter Veelaert ◽  
Wilfried Philips
Keyword(s):  
Feature Selection ◽  
Pedestrian Detection ◽  
Selection Criterion ◽  
Occlusion Handling ◽  
The Past ◽  
Reduction Techniques ◽  
Higher Dimensional ◽  
Selection Framework ◽  
New Feature ◽  
Dimension Reduction Techniques

Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned model. Current solutions typically exploit dimension reduction techniques. In this paper, we propose a simple but effective feature selection framework for pedestrian detection. Moreover, we introduce occluded pedestrian samples into the training process and combine it with a new feature selection criterion, which enables improved performances for occlusion handling problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our method over the state-of-art methods, especially for the occluded pedestrians.

Classification of EEG Signals Using Filter Bank Common Spatial Pattern Based on Fisher and Laplacian Criteria

2012 ◽  
Vol 239-240 ◽  
pp. 1033-1038
Author(s):  
Qing Guo Wei ◽  
Bin Wan ◽  
Zong Wu Lu
Keyword(s):  
Feature Selection ◽  
Spatial Pattern ◽  
Filter Bank ◽  
Selection Criterion ◽  
Classification Performance ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Frequency Bands ◽  
Operational Frequency ◽  
Laplacian Score

Common spatial pattern (CSP) is a highly successful algorithm in motor imagery based brain-computer interfaces (BCIs). The performance of the algorithm, however, depends largely on the operational frequency bands. To address the problem, a filter bank was applied to find optimal frequency bands. In filter bank, CSP was applied in all sub-band signals for feature extraction. The feature selection is the key of filter bank method for increasing classification performance. In this study, coefficient decimation (CD) technique was used to devise filter bank, while Fisher score and Laplacian score were proposed as feature selection criterion. In off-line analysis, the proposed method yielded relatively better cross-validation classification accuracies.

A Fast Feature Selection Method Based on Coefficient of Variation for Diabetics Prediction Using Machine Learning

2019 ◽  
Vol 1 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Tengyue Li ◽  
Simon Fong
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Coefficient Of Variation ◽  
Selection Process ◽  
Age Groups ◽  
Selection Criterion ◽  
Feature Selection Method ◽  
Selection Scheme ◽  
Large Populations ◽  
Process Computer

Diabetes has become a prevalent metabolic disease nowadays, affecting patients of all age groups and large populations around the world. Early detection would facilitate early treatment that helps the prognosis. In the literature of computational intelligence and medical care communities, different techniques have been proposed in predicting diabetes based on the historical records of related symptoms. The researchers share a common goal of improving the accuracy of a diabetes prediction model. In addition to the model induction algorithms, feature selection is a significant approach in retaining only the relevant attributes for the sake of building a quality prediction model later. In this article, a novel and simple feature selection criterion called Coefficient of Variation (CV) is proposed as a filter-based feature selection scheme. By following the CV method, attributes that have a data dispersion too low are disqualified from the model construction process. Thereby the attributes which are factors leading to poor model accuracy are discarded. The computation of CV is simple, hence enabling an efficient feature selection process. Computer simulation experiments by using the Prima Indian diabetes dataset is used to compare the performance of CV with other traditional feature selection methods. Superior results by CV are observed.

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