The Hybrid Filter Feature Selection Methods for Improving High-Dimensional Text Categorization

Nam, Le Nguyen Hoai; Quoc, Ho Bao

doi:10.1142/s021848851750009x

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…The statistical comparison includes parametric and non-parametric. In this study, we used both t-test, a parametric statistical comparison, and Wilcoxon signed-ranks test, a nonparametric statistical comparison, as in [43], [54], and [55]. Ttest depends on t-value while Wilcoxon signed-ranks test depends on z-value.…”

Section: Tools-home Improvement Datasetmentioning

confidence: 99%

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Nam

2022

IEEE Access

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To meet the increasing demand for group activities, single-user recommender systems need to be scaled up so as to provide recommendations to groups of users. This issue is solved by aggregating item preference profiles of individual group members into a single item preference profile, thereby allowing recommendations to be created for this item preference profile. In this paper, we introduce the concept of deep profiles of users, and we propose group recommendation methods based on the aggregation of group members' deep profiles, instead of item preference profiles as in previous studies. The term deep profile refers to the users' profiles that lie deep within the recommendation algorithms. Experiments have shown that group recommendations based on deep profiles give higher efficiency in terms of F1-score and nDCG than those based on item preference profiles. INDEX TERMSCollaborative filtering; group recommender systems; recommender systems I. INTRODUCTION

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Section: Tools-home Improvement Datasetmentioning

confidence: 99%

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Nam

2022

IEEE Access

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“…The statistical comparison includes parametric and non‐parametric methods. In this study, we used both the t ‐test, a parametric statistical comparison, and the Mann–Whitney U test, a non‐parametric statistical comparison 54–56 . These pairwise statistical comparisons include the averages (

μ normals

) of the evaluation results (SSE, MAE, MBRE, MIBRE, MdMRE, and RMSE) from the five‐fold cross‐validations of the four experimental datasets.…”

Section: Introductionmentioning

confidence: 99%

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Nhung,

Van Hai,

Silhavy

et al. 2023

J Software Evolu Process

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Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p‐value for the t‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

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“…e news popularity prediction studied in this paper is to predict the number of pageviews or retweets that may be obtained after the news is released. It can help journalists to better evaluate the quality of news and rank news, so as to conduct news delivery more reasonably [17][18][19][20]. Online news popularity prediction is an extremely challenging task.…”

Section: Introductionmentioning

confidence: 99%

Prediction of News Popularity Based on Deep Neural Network

Cai

Zhang²

2022

Scientific Programming

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Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance.

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The Hybrid Filter Feature Selection Methods for Improving High-Dimensional Text Categorization

Cited by 12 publications

References 19 publications

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Prediction of News Popularity Based on Deep Neural Network

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