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Information-Theoretic Methods in Data Analytics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 17 August 2024 | Viewed by 8158

Special Issue Editor

Department of Industrial Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: social data mining; bio data mining; bioinformatics with statistical learning; time series; computational and wavelet methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information-theoretic methods in data analytics are an important and basic research tool to solve practical problems under uncertain situations. They serve as a fundamental building block in modern data mining, machine learning, pattern recognition, and deep learning, among other fields, as well as in classical data modeling. This research direction has received consistent attention from both academia and industry. Despite the necessity and success of information-based methods, the research community still needs to share information-based paradigms and their applications.

This Special Issue aims to collect works on novel information-driven methods and their applications, hopefully with emphasis on statistical frameworks and flows, in numerous domains, such as medicine, finance, business, biology, marketing, education, etc. Works that include topics such as information, entropy, statistical inference, data compression, feature selection and extraction, discovery of clusters and/or communities in association with prediction, outlier detection, association rule mining, recommendation systems, reinforcement learning, pattern recognition, deep neural networks, and other statistical and analytical topics based on data are of particular interest.

Dr. Kichun Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information
  • probability
  • divergence
  • statistical inference
  • data compression
  • data visualization
  • community detection
  • outlier detection
  • feature selection
  • data mining
  • machine learning
  • pattern recognition
  • neural networks
  • applications of data analysis

Published Papers (6 papers)

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Research

19 pages, 7779 KiB  
Article
A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network
by Pengcheng Li, Baotian Dong and Sixian Li
Entropy 2024, 26(5), 390; https://doi.org/10.3390/e26050390 - 30 Apr 2024
Viewed by 552
Abstract
Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, [...] Read more.
Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network’s operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model’s correlation. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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16 pages, 510 KiB  
Article
A Comprehensive Framework for Measuring the Immediate Impact of TV Advertisements: TV-Impact
by Afra Arslan, Koray Tecimer, Hacer Turgut, Ömür Bali, Arda Yücel, Gülfem Isiklar Alptekin and Günce Keziban Orman
Entropy 2024, 26(2), 109; https://doi.org/10.3390/e26020109 - 25 Jan 2024
Viewed by 1568
Abstract
Measuring the immediate impact of television advertisements (TV ads) on online traffic poses significant challenges in many aspects. Nonetheless, a comprehensive consideration is essential to fully grasp consumer reactions to TV ads. So far, the measurement of this effect has not been studied [...] Read more.
Measuring the immediate impact of television advertisements (TV ads) on online traffic poses significant challenges in many aspects. Nonetheless, a comprehensive consideration is essential to fully grasp consumer reactions to TV ads. So far, the measurement of this effect has not been studied to a large extent. Existing studies have either determined how a specific focus group, i.e., toddlers, people of a certain age group, etc., react to ads via simple statistical tests using a case study approach or have examined the effects of advertising with simple regression models. This study introduces a comprehensive framework called TV-Impact. The framework uses a Bayesian structural time-series model called CausalImpact. There are additional novel approaches developed within the framework. One of the novelties of TV-Impact lies in its dynamic algorithm for selecting control variables which are supporting data sources and presumed to be unaffected by TV ads. In addition, we proposed the concept of Group Ads to combine overlapping ads into a single ad structure. Then, Random Forest Regressor, which is a commonly preferred supervised learning method, is used to decompose the impact into single ads. The TV-Impact framework was applied to the data of iLab, a venture company in Turkey, and manages its companies’ advertising strategies. The findings reveal that the TV-Impact model positively influenced the companies’ strategies for allocating their TV advertisement budgets and increased the amount of traffic driven to company websites, serving as an effective decision support system. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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15 pages, 1153 KiB  
Article
Information Difference of Transfer Entropies between Head Motion and Eye Movement Indicates a Proxy of Driving
by Runlin Zhang, Qing Xu, Shunbo Wang, Simon Parkinson and Klaus Schoeffmann
Entropy 2024, 26(1), 3; https://doi.org/10.3390/e26010003 - 19 Dec 2023
Viewed by 990
Abstract
Visual scanning is achieved via head motion and gaze movement for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The coordination of the head and eyes is an important human behavior to [...] Read more.
Visual scanning is achieved via head motion and gaze movement for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The coordination of the head and eyes is an important human behavior to make a key contribution to goal-directed visual scanning and sensorimotor driving. In this paper, we basically investigate the two most common patterns in eye–head coordination: “head motion earlier than eye movement” and “eye movement earlier than head motion”. We utilize bidirectional transfer entropies between head motion and eye movements to determine the existence of these two eye–head coordination patterns. Furthermore, we propose a unidirectional information difference to assess which pattern predominates in head–eye coordination. Additionally, we have discovered a significant correlation between the normalized unidirectional information difference and driving performance. This result not only indicates the influence of eye–head coordination on driving behavior from a computational perspective but also validates the practical significance of our approach utilizing transfer entropy for quantifying eye–head coordination. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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15 pages, 1557 KiB  
Article
Anomaly Detection Using an Ensemble of Multi-Point LSTMs
by Geonseok Lee, Youngju Yoon and Kichun Lee
Entropy 2023, 25(11), 1480; https://doi.org/10.3390/e25111480 - 26 Oct 2023
Viewed by 1200
Abstract
As technologies for storing time-series data such as smartwatches and smart factories become common, we are collectively accumulating a great deal of time-series data. With the accumulation of time-series data, the importance of time-series abnormality detection technology that detects abnormal patterns such as [...] Read more.
As technologies for storing time-series data such as smartwatches and smart factories become common, we are collectively accumulating a great deal of time-series data. With the accumulation of time-series data, the importance of time-series abnormality detection technology that detects abnormal patterns such as Cyber-Intrusion Detection, Fraud Detection, Social Networks Anomaly Detection, and Industrial Anomaly Detection is emerging. In the past, time-series anomaly detection algorithms have mainly focused on processing univariate data. However, with the development of technology, time-series data has become complicated, and corresponding deep learning-based time-series anomaly detection technology has been actively developed. Currently, most industries rely on deep learning algorithms to detect time-series anomalies. In this paper, we propose an anomaly detection algorithm with an ensemble of multi-point LSTMs that can be used in three cases of time-series domains. We propose our anomaly detection model that uses three steps. The first step is a model selection step, in which a model is learned within a user-specified range, and among them, models that are most suitable are automatically selected. In the next step, a collected output vector from M LSTMs is completed by stacking ensemble techniques of the previously selected models. In the final step, anomalies are finally detected using the output vector of the second step. We conducted experiments comparing the performance of the proposed model with other state-of-the-art time-series detection deep learning models using three real-world datasets. Our method shows excellent accuracy, efficient execution time, and a good F1 score for the three datasets, though training the LSTM ensemble naturally requires more time. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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18 pages, 29855 KiB  
Article
Sentiment Analysis on Online Videos by Time-Sync Comments
by Jiangfeng Li, Ziyu Li, Xiaofeng Ma, Qinpei Zhao, Chenxi Zhang and Gang Yu
Entropy 2023, 25(7), 1016; https://doi.org/10.3390/e25071016 - 2 Jul 2023
Cited by 1 | Viewed by 1436
Abstract
Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to [...] Read more.
Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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18 pages, 1064 KiB  
Article
Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method
by Changlu Zhang, Haojie Fan, Jian Zhang, Qiong Yang and Liqian Tang
Entropy 2023, 25(6), 935; https://doi.org/10.3390/e25060935 - 13 Jun 2023
Viewed by 1336
Abstract
Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. [...] Read more.
Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted. Full article
(This article belongs to the Special Issue Information-Theoretic Methods in Data Analytics)
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