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Feature Selection and Data Sampling Methods for Learning Reputation Dimensions

Cristina Gârbacea, Manos Tsagkias, Maarten de Rijke
2014 Conference and Labs of the Evaluation Forum  
We explore three sampling strategies for selecting training examples, and probe their effect on classification performance.  ...  We find that all our submitted runs outperform the baseline, and that elaborate feature selection methods coupled with balanced datasets help improve classification accuracy.  ...  Our main research question is how we can use machine learning to extract and select discriminative features that can help us learn to classify the reputation dimension of a tweet.  ... 
dblp:conf/clef/GarbaceaTR14 fatcat:nk52gyy2nfgjbh47qpgcfayp6m

Improve Reputation Evaluation of Crowdsourcing Participants Using Multidimensional Index and Machine Learning Techniques

Yanrong Huang, Min Chen
2019 IEEE Access  
INDEX TERMS Crowdsourcing participants, reputation evaluation, machine learning, random forest, data dimension reduction.  ...  dimensionality reduction methods, including Linear Discriminant Analysis, Principal Component Analysis, Mean Impact Value method and ReliefF feature selection method, are used to eliminate redundant variables  ...  ACKNOWLEDGMENTS The authors would like to thank the editor and the anonymous reviewers for their insightful comments and constructive suggestions.  ... 
doi:10.1109/access.2019.2933147 fatcat:srvdgx5zirgibibruipazzdx5e

Using Reduced Set of Features to Detect Spam in Twitter Data with Decision Tree and KNN Classifier Algorithms

In our proposed work these algorithms are used to check data samples are spam samples or ham samples. In this methodology, we have used Twitter dataset for testing proposed approach.  ...  The researchers used more number of features to construct the models. Generally the original dataset contains many irrelevant and redundant features.  ...  [18] , studied feature selection approach for text classification. They had described various feature selection strategies and dimension reduction strategies to select appropriate features.  ... 
doi:10.35940/ijitee.f3616.078919 fatcat:vhkjdg2cprcvbmsyunk627qvqq

Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation [article]

Anton Akusok, Emil Eirola
2019 arXiv   pre-print
This work studies efficient non-iterative and iterative methods suitable for such data, evaluating the results on two representative machine learning tasks with millions of samples and features.  ...  The big data trend has inspired feature-driven learning tasks, which cannot be handled by conventional machine learning models.  ...  Acknowledgements This work was supported by Tekes -the Finnish Funding Agency for Innovation -as part of the "Cloud-assisted Security Services" (CloSer) project.  ... 
arXiv:1912.08616v1 fatcat:jadqegkygfeexn4m55zs7vo424

An Intrusion Detection Model for Wireless Sensor Network Based on Information Gain Ratio and Bagging Algorithm

Rui-Hong Dong, Hou-Hua Yan, Qiu-Yu Zhang
2020 International Journal of Network Security  
Firstly, the information gain ratio method is used to select the feature of sensor node traffic data in this model.  ...  Aiming at the problem that the dimension of the traffic data to be processed in the wireless sensor network (WSN) intrusion detection method is too high, which leads to the large amounts of computational  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
dblp:journals/ijnsec/DongYZ20 fatcat:x3colr5novc2zgtyden5z2ifbq

The Influences of CSR's Multi-Dimensional Characteristics on Firm Value Determination by a Fusion Approach

Kuang-Hua Hu, Sin-Jin Lin, Jau-Yang Liu, Fu-Hsiang Chen, Shih-Han Chen
2018 Sustainability  
Thus, this study breaks down CSR into numerous dimensions and further examines each dimension's impact on firm value.  ...  By doing so, managers can allocate their firm's valuable resources to suitable areas so as to increase its reputation and value.  ...  However, most related works that considered feature selection are based on one pre-decided method.  ... 
doi:10.3390/su10113872 fatcat:ntxqukjaw5cnbd52g33p6jptju

Identification High Influential Articles by Considering the Topic Characteristics of Articles

Mingyang Wang, Shijia Jiao, Jiaqi Zhang, Xiangrong Zhang, Na Zhu
2020 IEEE Access  
It is a parallel integrated learning method, which uses multiple trees for training and prediction, and outputs prediction value combined with training results [123] .  ...  For n samples X = (x 1 , x 2 , . . . , x n ) in the data sets X , {F 1 , F 2 , . . . F m } are m features of the data sets, {f 1 , f 2 , . . . , f m } are m feature vectors.  ... 
doi:10.1109/access.2020.3001190 fatcat:26r2mzos5fdynbk35y5vazifcy

Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm

Guo Yangyudongnanxin, Rahman Ali
2021 Scientific Programming  
We compared the proposed method with two other methods, and the experimental results show that the proposed method has higher classification accuracy of financial credit data and the risk assessment threshold  ...  The weighted random forest algorithm is used to classify the financial credit risk data, construct the evaluation index system, and use the analytic hierarchy process to evaluate the financial credit risk  ...  Acknowledgments is work was supported by the Provincial Soft Science Key Project (2013ZK2024 and 2014GK3147) of the Science and Technology Department of Hunan Province.  ... 
doi:10.1155/2021/6276155 fatcat:xnmkettvmffxzlph3i2wg2bykq

Overview of RepLab 2014: Author Profiling and Reputation Dimensions for Online Reputation Management [chapter]

Enrique Amigó, Jorge Carrillo-de-Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Edgar Meij, Maarten de Rijke, Damiano Spina
2014 Lecture Notes in Computer Science  
New data collections were provided for the development and evaluation of systems that participated in this benchmarking activity.  ...  This paper describes the organisation and results of RepLab 2014, the third competitive evaluation campaign for Online Reputation Management systems.  ...  Three sampling strategies were explored for selecting training examples.  ... 
doi:10.1007/978-3-319-11382-1_24 fatcat:ywwjzb6j5rb2xcgen4pxzt5dqi

Prediction of Sports Performance Combined with Deep Learning Model and Analysis of Influencing Factors

H. ZhaoriGetu
2022 Scientific Programming  
Through four dimensions related to the influence of sports achievements-interest in seeking knowledge, ability pursuit, altruistic orientation, and reputation acquisition, this paper studie [...]  ...  Sports also provide students with numerous social, psychological, and emotional benefits, which in turn improve their learning status and academic achievements.  ...  First, select the indicators that need to be predicted, input the relevant network parameters, and then input the data of sampling samples after the input, in which the sample data is a new sample [18  ... 
doi:10.1155/2022/4082906 doaj:5031fc9c20d2454ba147dde1c3328670 fatcat:ytgw7vqfnreq5gmh2vwc5wecui

Machine Learning based Trust Computational Model for IoT Services

Upul Jayasinghe, Gyu Myoung Lee, Tai-Won Um, Qi Shi
2018 IEEE Transactions on Sustainable Computing  
Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making.  ...  The results show that our method has advantages over other aggregation methods. Recommended for acceptance by M. Qiu and S.-Y. Kung.  ...  For both ML experiments here, two features out of five are selected at a time for the sake of demonstration purposes, as it is not feasible to show a five-dimension vector.  ... 
doi:10.1109/tsusc.2018.2839623 dblp:journals/tsusc/JayasingheLUS19 fatcat:qabxb2nmfratrobx2oxkzylryy

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

Qi Song, Yourui Huang
2021 IEEE Access  
dimension reduction data for Random Forest (RF) identification.  ...  Specifically, it first sets up a LIF spectrum acquisition system for liquor in the laboratory, uses Principal Component Analysis (PCA) to reduce the dimension of LIF spectral data, and then collects the  ...  The above advantages make it suitable for high-dimensional small sample data [25] , and can automatically realize the feature selection function.  ... 
doi:10.1109/access.2021.3049941 fatcat:omz3wtlh7jepzd5rbcu5cjppsm

Elastic Cloud Logs Traces, Storing and Replaying for Deep Machine Learning

Tariq Daradkeh, Anjali Agarwal, Nishith Goel, Jim Kozlowski
2020 Procedia Computer Science  
These two methods have a good reputation in processing data set for different goals in machine learning methods like image recognition, signal processing and prediction.  ...  These two methods have a good reputation in processing data set for different goals in machine learning methods like image recognition, signal processing and prediction.  ...  Acknowledgements The authors would like to acknowledge the financial support provided by Natural Sciences and Engineering Research Council (NSERC)'s CRD Grant in collaboration with Cistech Limited, Canada  ... 
doi:10.1016/j.procs.2020.04.011 fatcat:ehfmytdyyraoji6ourijcla7ey

A Survey on Trust Evaluation Based on Machine Learning

Jingwen Wang, Xuyang Jing, Zheng Yan, Yulong Fu, Witold Pedrycz, Laurence T. Yang
2020 ACM Computing Surveys  
Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic.  ...  It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation.  ...  The data indicators used for reputation evaluation come from four dimensions: initial reputation dimension, penalty dimension, evaluation dimension, and transaction dimension.  ... 
doi:10.1145/3408292 fatcat:fem3px673bcfdltackc7bstxji

Research on Network Active Detection Technology of Power Monitoring System Based on Machine Learning

Shi-shun ZHU, Yong ZHANG, Yu HAN, Jiang ZHU, Yao-qi LI
2019 DEStech Transactions on Engineering and Technology Research  
The scheme analyzes and processes the data, uses feature engineering to process the features, and improves the common filtering method by using the feature correlation matrix in feature selection.  ...  There are still many security risks in the current power monitoring system network, and lack of independent learning ability.  ...  Acknowledgement This research was financially supported by the Science and Technology Project of SGCC: Research and Application of Network Security Situation Awareness Technology in Power Monitoring System  ... 
doi:10.12783/dtetr/amsms2019/31864 fatcat:nggrclrgn5cxnhmvhos4fpelea
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