Nazha Selmaoui-Folcher, Jannaï Tokotoko, Samuel Gorohouna, Laïsa Roi, C. Leschi, Catherine Ris
Pretopology is a mathematical model developed from a weakening of the topological axiomatic. It was initially used in economic, social and biological sciences and next in pattern recognition and image analysis. More recently, it has been applied to the analysis of complex networks. Pretopology enables to work in a mathematical framework with weak properties, and its nonidempotent operator called pseudo-closure permits to implement iterative algorithms. It proposes a formalism that generalizes graph theory concepts and allows to model problems universally. In this paper, authors will extend this mathematical model to analyze complex data with spatiotemporal dimensions. Authors define the notion of a temporal pretopology based on a temporal function. They give an example of temporal function based on a binary relation, and construct a temporal pretopology. They define two new notions of temporal substructures which aim at representing evolution of substructures. They propose algorithms to extract these substructures. They experiment the proposition on 2 data and two economic real data.
{"title":"Concept of Temporal Pretopology for the Analysis for Structural Changes: Application to Econometrics","authors":"Nazha Selmaoui-Folcher, Jannaï Tokotoko, Samuel Gorohouna, Laïsa Roi, C. Leschi, Catherine Ris","doi":"10.4018/ijdwm.298004","DOIUrl":"https://doi.org/10.4018/ijdwm.298004","url":null,"abstract":"Pretopology is a mathematical model developed from a weakening of the topological axiomatic. It was initially used in economic, social and biological sciences and next in pattern recognition and image analysis. More recently, it has been applied to the analysis of complex networks. Pretopology enables to work in a mathematical framework with weak properties, and its nonidempotent operator called pseudo-closure permits to implement iterative algorithms. It proposes a formalism that generalizes graph theory concepts and allows to model problems universally. In this paper, authors will extend this mathematical model to analyze complex data with spatiotemporal dimensions. Authors define the notion of a temporal pretopology based on a temporal function. They give an example of temporal function based on a binary relation, and construct a temporal pretopology. They define two new notions of temporal substructures which aim at representing evolution of substructures. They propose algorithms to extract these substructures. They experiment the proposition on 2 data and two economic real data.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79691839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wissam Siblini, Mohamed Challal, Charlotte Pasqual
Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although Transformer-based language models such as Bert have shown an ability to outperform humans to extract answers from small pre-selected passages of text, they suffer from their high complexity if the search space is much larger. The most common way to deal with this problem is to add a preliminary information retrieval step to strongly filter the corpus and keep only the relevant passages. In this article, the authors consider a more direct and complementary solution which consists in restricting the attention mechanism in Transformer-based models to allow a more efficient management of computations. The resulting variants are competitive with the original models on the extractive task and allow, in the ODQA setting, a significant acceleration of predictions and sometimes even an improvement in the quality of response.
{"title":"Efficient Open Domain Question Answering With Delayed Attention in Transformer-Based Models","authors":"Wissam Siblini, Mohamed Challal, Charlotte Pasqual","doi":"10.4018/ijdwm.298005","DOIUrl":"https://doi.org/10.4018/ijdwm.298005","url":null,"abstract":"Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although Transformer-based language models such as Bert have shown an ability to outperform humans to extract answers from small pre-selected passages of text, they suffer from their high complexity if the search space is much larger. The most common way to deal with this problem is to add a preliminary information retrieval step to strongly filter the corpus and keep only the relevant passages. In this article, the authors consider a more direct and complementary solution which consists in restricting the attention mechanism in Transformer-based models to allow a more efficient management of computations. The resulting variants are competitive with the original models on the extractive task and allow, in the ODQA setting, a significant acceleration of predictions and sometimes even an improvement in the quality of response.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84632210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Romane Scherrer, Erwan Aulnette, T. Quiniou, J. Kasarhérou, Pierre Kolb, Nazha Selmaoui-Folcher
An autonomous acoustic system based on two bottom-moored hydrophones, a two-input audio board and a small single-board computer was installed at the entrance of a marina to detect entering/exiting boat. Windowed time lagged cross-correlations are calculated by the system to find the consecutive time delays between the hydrophone signals and to compute a signal which is a function of the boats' angular trajectories. Since its installation, the single-board computer performs online prediction with a signal processing-based algorithm which achieved an accuracy of 80 %. To improve system performance, a convolutional neural network (CNN) is trained with the acquired data to perform real-time detection. Two classification tasks were considered (binary and multiclass) to both detect a boat and its direction of navigation. Finally, a trained CNN was implemented in a single-board computer to ensure that prediction can be performed in real time.
{"title":"Boat Detection in Marina Using Time-Delay Analysis and Deep Learning","authors":"Romane Scherrer, Erwan Aulnette, T. Quiniou, J. Kasarhérou, Pierre Kolb, Nazha Selmaoui-Folcher","doi":"10.4018/ijdwm.298006","DOIUrl":"https://doi.org/10.4018/ijdwm.298006","url":null,"abstract":"An autonomous acoustic system based on two bottom-moored hydrophones, a two-input audio board and a small single-board computer was installed at the entrance of a marina to detect entering/exiting boat. Windowed time lagged cross-correlations are calculated by the system to find the consecutive time delays between the hydrophone signals and to compute a signal which is a function of the boats' angular trajectories. Since its installation, the single-board computer performs online prediction with a signal processing-based algorithm which achieved an accuracy of 80 %. To improve system performance, a convolutional neural network (CNN) is trained with the acquired data to perform real-time detection. Two classification tasks were considered (binary and multiclass) to both detect a boat and its direction of navigation. Finally, a trained CNN was implemented in a single-board computer to ensure that prediction can be performed in real time.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73432847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erwan Schild, Gautier Durantin, Jean-Charles Lamirel, F. Miconi
Chatbots represent a promising tool to automate the processing of requests in a business context. However, despite major progress in natural language processing technologies, constructing a dataset deemed relevant by business experts is a manual, iterative and error-prone process. To assist these experts during modelling and labelling, the authors propose an active learning methodology coined Interactive Clustering. It relies on interactions between computer-guided segmentation of data in intents, and response-driven human annotations imposing constraints on clusters to improve relevance.This article applies Interactive Clustering on a realistic dataset, and measures the optimal settings required for relevant segmentation in a minimal number of annotations. The usability of the method is discussed in terms of computation time, and the achieved compromise between business relevance and classification performance during training.In this context, Interactive Clustering appears as a suitable methodology combining human and computer initiatives to efficiently develop a useable chatbot.
{"title":"Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering","authors":"Erwan Schild, Gautier Durantin, Jean-Charles Lamirel, F. Miconi","doi":"10.4018/ijdwm.298007","DOIUrl":"https://doi.org/10.4018/ijdwm.298007","url":null,"abstract":"Chatbots represent a promising tool to automate the processing of requests in a business context. However, despite major progress in natural language processing technologies, constructing a dataset deemed relevant by business experts is a manual, iterative and error-prone process. To assist these experts during modelling and labelling, the authors propose an active learning methodology coined Interactive Clustering. It relies on interactions between computer-guided segmentation of data in intents, and response-driven human annotations imposing constraints on clusters to improve relevance.This article applies Interactive Clustering on a realistic dataset, and measures the optimal settings required for relevant segmentation in a minimal number of annotations. The usability of the method is discussed in terms of computation time, and the achieved compromise between business relevance and classification performance during training.In this context, Interactive Clustering appears as a suitable methodology combining human and computer initiatives to efficiently develop a useable chatbot.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73614194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.
{"title":"Large-Scale System for Social Media Data Warehousing: The Case of Twitter-Related Drug Abuse Events Integration","authors":"Ferdaous Jenhani, M. Gouider","doi":"10.4018/ijdwm.290890","DOIUrl":"https://doi.org/10.4018/ijdwm.290890","url":null,"abstract":"Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75167432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Wu, Lingyun Sun, Gautam Srivastava, Vicente García Díaz, Jerry Chun‐wei Lin
This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.
{"title":"A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System","authors":"J. Wu, Lingyun Sun, Gautam Srivastava, Vicente García Díaz, Jerry Chun‐wei Lin","doi":"10.4018/ijdwm.309957","DOIUrl":"https://doi.org/10.4018/ijdwm.309957","url":null,"abstract":"This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46827513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.
{"title":"Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering","authors":"Xin Lu, Donghong Gu, Haolan Zhang, Zhengxin Song, Qianhua Cai, Hongya Zhao, Haiming Wu","doi":"10.4018/ijdwm.307904","DOIUrl":"https://doi.org/10.4018/ijdwm.307904","url":null,"abstract":"Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81757483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cu Nguyen Giap, Nguyen Nhu Son, Long Giang Nguyen, Hoang Thi Minh Chau, Tran Manh Tuan, Le Hoang Son
It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.
{"title":"A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral","authors":"Cu Nguyen Giap, Nguyen Nhu Son, Long Giang Nguyen, Hoang Thi Minh Chau, Tran Manh Tuan, Le Hoang Son","doi":"10.4018/ijdwm.290891","DOIUrl":"https://doi.org/10.4018/ijdwm.290891","url":null,"abstract":"It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86618244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.
{"title":"Density-Based Spatial Anomalous Window Discovery","authors":"Prerna Mohod, V. Janeja","doi":"10.4018/ijdwm.299015","DOIUrl":"https://doi.org/10.4018/ijdwm.299015","url":null,"abstract":"The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86493401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanu Dayara, F. Thabtah, Hussein Abdel-jaber, S. Zeidan
One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.
{"title":"Crime Analyses Using Data Analytics","authors":"Thanu Dayara, F. Thabtah, Hussein Abdel-jaber, S. Zeidan","doi":"10.4018/ijdwm.299014","DOIUrl":"https://doi.org/10.4018/ijdwm.299014","url":null,"abstract":"One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88401411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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