Improved feature-selection method considering the imbalance problem in text categorization.
Ontology highlight
ABSTRACT:
SUBMITTER: Yang J
PROVIDER: S-EPMC4058251 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.
| S-EPMC5738058 | biostudies-other
Project description:ObjectiveThe US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.DesignWe selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.MeasurementsClassifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.ResultsRule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).ConclusionOur validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.
| S-EPMC3168300 | biostudies-literature
Project description:IntroductionCardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results.Materials and methodsFeature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH?<?7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance.ResultsThe experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively.ConclusionConsequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.
| S-EPMC6702252 | biostudies-literature
Project description:Mutual Information (MI) based feature selection methods are popular due to their ability to capture the nonlinear relationship among variables. However, existing works rarely address the error (bias) that occurs due to the use of finite samples during the estimation of MI. To the best of our knowledge, none of the existing methods address the bias issue for the high-order interaction term which is essential for better approximation of joint MI. In this paper, we first calculate the amount of bias of this term. Moreover, to select features using Electronic supplementary material The online version of this chapter (10.1007/978-3-030-47426-3_64) contains supplementary material, which is available to authorized users.
| S-EPMC7206174 | biostudies-literature
Project description:Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.
| S-EPMC5381872 | biostudies-literature
Project description:There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a goup of genes that mainly invovled in cell cylce were significanly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators.
2010-01-05 | GSE19662 | GEO
Project description:There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a goup of genes that mainly invovled in cell cylce were significanly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. For in vitro experiment, primary cultured rat hepatocytes were treated one of 105 compounds with relative controls. At least three biological replicates were used for each unique condition. In total 531 arrays were used.
2010-01-05 | E-GEOD-19662 | biostudies-arrayexpress
Project description:BACKGROUND:Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. OBJECTIVE:This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. METHODS:WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. RESULTS:This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. CONCLUSIONS:This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.
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Project description:Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.
| S-EPMC7877636 | biostudies-literature
Project description:High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA) in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor) on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma.
| S-EPMC4105478 | biostudies-literature