FEATURE SELECTION BASED ON MINIMUM ERROR MINIMAX PROBABILITY MACHINE

2007 ◽  
Vol 21 (08) ◽  
pp. 1279-1292 ◽  
Author(s):  
ZENGLIN XU ◽  
IRWIN KING ◽  
MICHAEL R. LYU
Keyword(s):  
Feature Selection ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Minimum Error ◽  
Automatic Balance ◽  
Classification Framework ◽  
Data Partitions ◽  
Minimax Probability Machine ◽  
Optimal Feature Subset

Feature selection is an important task in pattern recognition. Support Vector Machine (SVM) and Minimax Probability Machine (MPM) have been successfully used as the classification framework for feature selection. However, these paradigms cannot automatically control the balance between prediction accuracy and the number of selected features. In addition, the selected feature subsets are also not stable in different data partitions. Minimum Error Minimax Probability Machine (MEMPM) has been proposed for classification recently. In this paper, we outline MEMPM to select the optimal feature subset with good stability and automatic balance between prediction accuracy and the size of feature subset. The experiments against feature selection with SVM and MPM show the advantages of the proposed MEMPM formulation in stability and automatic balance between the feature subset size and the prediction accuracy.

The Research Based on GA-SVM Feature Selection Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1497-1502
Author(s):  
Hong Mei Li ◽  
Lin Gen Yang ◽  
Li Hua Zou
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Classification Accuracy ◽  
A Priori ◽  
Support Vector ◽  
Feature Subset ◽  
Accuracy Rate ◽  
Genetic Encoding ◽  
Optimal Feature Subset ◽  
Priori Information

To make feature subset which can gain the higher classification accuracy rate, the method based on genetic algorithms and the feature selection of support vector machine is proposed. Firstly, the ReliefF algorithm provides a priori information to GA, the parameters of the support vector machine mixed into the genetic encoding,and then using genetic algorithm finds the optimal feature subset and support vector machines parameter combination. Finally, experimental results show that the proposed algorithm can gain the higher classification accuracy rate based on the smaller feature subset.

scholarly journals m5CPred-SVM:  A Novel Method for Predicting m5C Sites of RNA

2020 ◽  
Author(s):  
Xiao Chen ◽  
Yi Xiong ◽  
Yinbo Liu ◽  
Yuqing Chen ◽  
Shoudong Bi ◽  
...  
Keyword(s):  
Cell Fate ◽  
Prediction Accuracy ◽  
Cytosine Methylation ◽  
Computational Method ◽  
Selection Strategy ◽  
Support Vector ◽  
Feature Subset ◽  
Accurate Identification ◽  
Benchmark Datasets ◽  
Optimal Feature Subset

Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functionssuch as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA,researcherscanbetter understandthe exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost.However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVMoffered substantially higher prediction accuracy thanpreviously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species.The result shows that our model outperformed the existing state-of-art models.Our model is available for users through a web serverat http://zhulab.ahu.edu.cn/m5CPred-SVM.

Reseach on Feature Selection Algorithm Based on the Margin of Support Vector Machine

2013 ◽  
Vol 333-335 ◽  
pp. 1430-1434
Author(s):  
Lin Fang Hu ◽  
Lei Qiao ◽  
Min De Huang
Keyword(s):  
Feature Selection ◽  
Weather Forecast ◽  
Support Vector ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Optimal Hyperplane ◽  
Optimal Feature Subset ◽  
Optimal Feature

A feature selection algorithm based on the optimal hyperplane of SVM is raised. Using the algorithm, the contribution to the classification of each feature in the candidate feature set is test, and then the feature subset with best classification ability will be selected. The algorithm is used in the recognition process of storm monomers in weather forecast, and experimental data show that the classification ability of the features can be effectively evaluated; the optimal feature subset is selected to enhance the working performance of the classifier.

scholarly journals Hybrid-Recursive Feature Elimination for Efficient Feature Selection

2020 ◽  
Vol 10 (9) ◽  
pp. 3211
Author(s):  
Hyelynn Jeon ◽  
Sejong Oh
Keyword(s):  
Feature Selection ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Feature Subset ◽  
Learning Tasks ◽  
Original Dataset ◽  
Representative Subset ◽  
Low Performance ◽  
Optimal Feature Subset ◽  
Efficient Selection

As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks. Highly dimensional datasets that have an excessive number of features can cause low performance in such tasks. Overfitting is a typical problem. In addition, datasets that are of high dimensionality can create shortages in space and require high computing power, and models fitted to such datasets can produce low classification accuracies. Thus, it is necessary to select a representative subset of features by utilizing an efficient selection method. Many feature selection methods have been proposed, including recursive feature elimination. In this paper, a hybrid-recursive feature elimination method is presented which combines the feature-importance-based recursive feature elimination methods of the support vector machine, random forest, and generalized boosted regression algorithms. From the experiments, we confirm that the performance of the proposed method is superior to that of the three single recursive feature elimination methods.

scholarly journals Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8370
Author(s):  
Ala Hag ◽  
Dini Handayani ◽  
Maryam Altalhi ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Vector Space ◽  
Frequency Domain ◽  
Mental Stress ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Feature Subset ◽  
Optimal Feature Subset ◽  
Optimal Feature

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

scholarly journals A novel feature selection algorithm based on damping oscillation theory

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255307
Author(s):  
Fujun Wang ◽  
Xing Wang
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Euclidean Distance ◽  
Oscillation Theory ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Data Sets ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Filter Model

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

scholarly journals m5CPred-SVM:  A Novel Method for Predicting m5C Sites of RNA

2020 ◽  
Author(s):  
Xiao Chen ◽  
Yi Xiong ◽  
Yinbo Liu ◽  
Yuqing Chen ◽  
Shoudong Bi ◽  
...  
Keyword(s):  
Cell Fate ◽  
Prediction Accuracy ◽  
Cytosine Methylation ◽  
Low Cost ◽  
Computational Method ◽  
Selection Strategy ◽  
Support Vector ◽  
Feature Subset ◽  
Biological Functions ◽  
Accurate Identification

Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at http://zhulab.ahu.edu.cn/m5CPred-SVM.

A New Hybrid Feature Subset Selection Framework Based on Binary Genetic Algorithm and Information Theory

2019 ◽  
Vol 18 (03) ◽  
pp. 1950020 ◽  
Author(s):  
Alok Kumar Shukla ◽  
Pradeep Singh ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification Accuracy ◽  
B Cell Lymphoma ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Subset ◽  
Binary Genetic Algorithm

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

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