Feature selection for text classification: A review

Deng, Xiangquan; Li, Yuqing; Weng, Jian; Zhang, Jilian

doi:10.1007/s11042-018-6083-5

Cited by 259 publications

(127 citation statements)

References 82 publications

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“…K-means clustering has been shown to work well for large-scale data and its accuracy level is also high compared to other clustering algorithms [68]. The K-means clustering algorithm collects the extracted terms according to their feature values into K number clusters, and K is any positive number that is used to determine the number of clusters.…”

Section: K-means Clusteringmentioning

confidence: 99%

Spam Review Detection Techniques: A Systematic Literature Review

et al. 2019

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Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

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Section: K-means Clusteringmentioning

confidence: 99%

Spam Review Detection Techniques: A Systematic Literature Review

et al. 2019

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“…Three feature selection algorithms, namely, filter model [29], wrapper model with recursive feature elimination (RFE) algorithm [30,31], and RF model [32,33], are applied to select the features most relevant to the gearbox oil temperature as the input variables of the model. These three algorithms are typical feature selection methods based on how to generate feature subsets [34]. They will calculate the most relevant features according to different rules, which can avoid the defect of a single method.…”

Section: Feature Selectionmentioning

confidence: 99%

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

Yuan

Sun

Ma³

2019

Energies

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The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.

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“…Feature selection methods mostly involve iterations of two steps until their stopping criterion is met (Deng et al, 2008). The two iterative steps are feature subset selection and evaluation of performance of the chosen subset (Deng et al, 2008).…”

Section: Application Backgroundmentioning

confidence: 99%

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

Ahmadizadeh

Pousett²,

Menon

2019

Front. Bioeng. Biotechnol.

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Background: Various human machine interfaces (HMIs) are used to control prostheses, such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control. Motivation: The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This study assessed the use of multiple channel selection (CS) methods for this purpose. Methods: In this case study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta. Results: Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets. The three selected methods were also compared in terms of stability [i.e., consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)]. Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this study. Conclusion: This study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.

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Feature selection for text classification: A review

Cited by 259 publications

References 82 publications

Spam Review Detection Techniques: A Systematic Literature Review

Spam Review Detection Techniques: A Systematic Literature Review

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

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