Feature selection methods facilitate removal of irrelevant attributes. Ineffective features may contain outliers that degrade performance of classifiers. We propose an ensemble filter base feature selection technique for multiclass classification. The technique combines results of four selection methods to create an ensemble list. The study uses a red wine dataset drawn from UC Irvine machine learning data repository and WEKA, a collection of machine learning algorithms for data mining tasks. The multiclass red wine dataset is binarized using WekaMulticlassClassifier utilizing the 1against 1 with pairwise coupling decomposing scheme. Using random forest algorithm and root mean square error values, a learning curve is generated that establishes an optimal ensemble sub-list. Outliers are detected using the Tukey statistical method. The proposed ensemble method outperformed the single feature methods. The study concludes by showing that unnecessary features and presence of outliers degrades classifiers performance. We recommend further studies on the effect of gradual selective removal of outliers on classification.
{"title":"An Ensemble Filter Feature Selection Method and Outlier Detection Method for Multiclass Classification","authors":"Dalton Ndirangu, W. Mwangi, L. Nderu","doi":"10.1145/3316615.3318223","DOIUrl":"https://doi.org/10.1145/3316615.3318223","url":null,"abstract":"Feature selection methods facilitate removal of irrelevant attributes. Ineffective features may contain outliers that degrade performance of classifiers. We propose an ensemble filter base feature selection technique for multiclass classification. The technique combines results of four selection methods to create an ensemble list. The study uses a red wine dataset drawn from UC Irvine machine learning data repository and WEKA, a collection of machine learning algorithms for data mining tasks. The multiclass red wine dataset is binarized using WekaMulticlassClassifier utilizing the 1against 1 with pairwise coupling decomposing scheme. Using random forest algorithm and root mean square error values, a learning curve is generated that establishes an optimal ensemble sub-list. Outliers are detected using the Tukey statistical method. The proposed ensemble method outperformed the single feature methods. The study concludes by showing that unnecessary features and presence of outliers degrades classifiers performance. We recommend further studies on the effect of gradual selective removal of outliers on classification.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123782559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aspect sentiment analysis is a fine-gained task in sentiment analysis. In this paper, we propose a novel LSTM network model, which combines multi-attention and aspect contexts, i.e. LSTM-MATT-AC. Multi-attention mechanism that integrates the factors of location, content and class could adaptively capture important information in the contexts with the supervision of aspect targets. In other words, the model is more robust against irrelevant information. Simultaneously, aspect context mechanism extends differentiate left and right contexts given aspect targets and strengthens the expressive power of the model for handling more complication by mining deeper semantic information. Experiment results on SemEval2014 Task4 and Twitter datasets show that the accuracy of sentiment classification reaches 80.6%, 75.1% and 71.1% respectively. Compared to previous neural network-based sentiment analysis models, the accuracy has been further improved.
方面情感分析是情感分析中的一项精细任务。本文提出了一种结合多注意和方面上下文的LSTM网络模型，即LSTM- matt - ac。多注意机制融合了地点、内容和类别等因素，能够在方面目标的监督下自适应地捕捉情境中的重要信息。换句话说，模型对不相关信息的鲁棒性更强。同时，方面上下文机制扩展了在给定方面目标的情况下区分左右上下文的能力，并通过挖掘更深层次的语义信息增强了模型的表达能力，以处理更复杂的问题。在SemEval2014 Task4和Twitter数据集上的实验结果表明，情感分类的准确率分别达到80.6%、75.1%和71.1%。与以往基于神经网络的情感分析模型相比，精度得到了进一步提高。
{"title":"Multi-Attention Network for Aspect Sentiment Analysis","authors":"Huiyu Han, Xiaoge Li, Shuting Zhi, Haoyue Wang","doi":"10.1145/3316615.3316673","DOIUrl":"https://doi.org/10.1145/3316615.3316673","url":null,"abstract":"Aspect sentiment analysis is a fine-gained task in sentiment analysis. In this paper, we propose a novel LSTM network model, which combines multi-attention and aspect contexts, i.e. LSTM-MATT-AC. Multi-attention mechanism that integrates the factors of location, content and class could adaptively capture important information in the contexts with the supervision of aspect targets. In other words, the model is more robust against irrelevant information. Simultaneously, aspect context mechanism extends differentiate left and right contexts given aspect targets and strengthens the expressive power of the model for handling more complication by mining deeper semantic information. Experiment results on SemEval2014 Task4 and Twitter datasets show that the accuracy of sentiment classification reaches 80.6%, 75.1% and 71.1% respectively. Compared to previous neural network-based sentiment analysis models, the accuracy has been further improved.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122657789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Realizability checking is used to detect flaws in reactive system specifications that are difficult for humans to find. However, these checks are computationally costly. To address this problem, researchers have studied efficient methods for implementing such checking procedures. In this paper, we propose a new implementation method of realizability checking. While symbolic approaches have been adopted in many previous methods, we take a partially symbolic approach, in which binary decision diagrams (BDDs) are used partially. We developed a prototype realizability checker based on our method, and experimentally compared it to tools based on other implementation methods. Our prototype was efficient in comparison to the other tools.
{"title":"Towards Efficient Implementation of Realizability Checking for Reactive System Specifications","authors":"Masaya Shimakawa, Atsushi Ueno, Shohei Mochizuki, Takashi Tomita, Shigeki Hagihara, N. Yonezaki","doi":"10.1145/3316615.3316634","DOIUrl":"https://doi.org/10.1145/3316615.3316634","url":null,"abstract":"Realizability checking is used to detect flaws in reactive system specifications that are difficult for humans to find. However, these checks are computationally costly. To address this problem, researchers have studied efficient methods for implementing such checking procedures. In this paper, we propose a new implementation method of realizability checking. While symbolic approaches have been adopted in many previous methods, we take a partially symbolic approach, in which binary decision diagrams (BDDs) are used partially. We developed a prototype realizability checker based on our method, and experimentally compared it to tools based on other implementation methods. Our prototype was efficient in comparison to the other tools.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122761587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Delivery route optimization for logistic industry is one of applications proposed on smart meter infrastructure. It's expected to drastically reduce absent-delivery which amounts to 20% of total delivery in Japan, which estimated to save $billions a year. But as previous works pointed out, the concern on user privacy is the biggest hurdle yet to be addressed. In this research, we proposed a new approach to improve user privacy by converting electricity data into route data and optimize it before providing to service provider. Then, we tested pragmatic privacy improvement and route optimization through actual delivery experiment. Results showed that the information leakage rate (# of absence detection per delivery) decreased from 23% to 4% by this system and decreased to 2% with additional operational change. Also, the experiment validated decrease of absent-delivery rate from 23% to 2% and travel distance by 5% while improving privacy. Applying adequate method to "delivery optimization through occupancy prediction" enabled achieving both user privacy and absent-delivery reduction significantly.
{"title":"Privacy Enhancement for Delivery Route Optimization through Occupancy Prediction","authors":"Shimpei Ohsugi, Kenji Tanaka, N. Koshizuka","doi":"10.1145/3316615.3316625","DOIUrl":"https://doi.org/10.1145/3316615.3316625","url":null,"abstract":"Delivery route optimization for logistic industry is one of applications proposed on smart meter infrastructure. It's expected to drastically reduce absent-delivery which amounts to 20% of total delivery in Japan, which estimated to save $billions a year. But as previous works pointed out, the concern on user privacy is the biggest hurdle yet to be addressed. In this research, we proposed a new approach to improve user privacy by converting electricity data into route data and optimize it before providing to service provider. Then, we tested pragmatic privacy improvement and route optimization through actual delivery experiment. Results showed that the information leakage rate (# of absence detection per delivery) decreased from 23% to 4% by this system and decreased to 2% with additional operational change. Also, the experiment validated decrease of absent-delivery rate from 23% to 2% and travel distance by 5% while improving privacy. Applying adequate method to \"delivery optimization through occupancy prediction\" enabled achieving both user privacy and absent-delivery reduction significantly.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133298985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main construction method of the current ontology is to rely on ontology experts for manual construction. Because manual construction requires a lot of manual participation, manual construction has great limitations. Text data as one of the main forms of data source, how to construct domain ontology automatically from texts and how to provide semantic retrieval support to text quickly by ontology is the hotspot of ontology research at present. Aiming at the above problems, an automatic construction method of domain ontology based on knowledge graph and association rule mining is presented, and it can extract the concepts, hierarchies and non-hierarchies of domain ontology from text, and finally form ontology by Jena. It also provides semantic retrieval of text by associating text and concepts in the process of ontology construction. Finally, the effect of automatic ontology construction is verified by the effect of text retrieval.
{"title":"Research on Domain Ontology Automation Construction Based on Chinese Texts","authors":"Bo Wang, Junwei Luo, Shuyuan Zhu","doi":"10.1145/3316615.3316685","DOIUrl":"https://doi.org/10.1145/3316615.3316685","url":null,"abstract":"The main construction method of the current ontology is to rely on ontology experts for manual construction. Because manual construction requires a lot of manual participation, manual construction has great limitations. Text data as one of the main forms of data source, how to construct domain ontology automatically from texts and how to provide semantic retrieval support to text quickly by ontology is the hotspot of ontology research at present. Aiming at the above problems, an automatic construction method of domain ontology based on knowledge graph and association rule mining is presented, and it can extract the concepts, hierarchies and non-hierarchies of domain ontology from text, and finally form ontology by Jena. It also provides semantic retrieval of text by associating text and concepts in the process of ontology construction. Finally, the effect of automatic ontology construction is verified by the effect of text retrieval.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122133001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a learner -- centered approach of the Classcraft gamification app, the learning approach of students was evaluated based on the behaviorism learning theory. The assessment of the gamification app was based on two criteria: Game elements of the gamification app and the Evaluation criteria in student learning (self- assessment and instructor's assessment). The gamification app was introduced to students to evaluate the learning capacity and its effect to students.
{"title":"Assessing Classcraft as an Effective Gamification App based on Behaviorism Learning Theory","authors":"F. Eugenio, Ardhee Joy T. Ocampo","doi":"10.1145/3316615.3316669","DOIUrl":"https://doi.org/10.1145/3316615.3316669","url":null,"abstract":"This paper presents a learner -- centered approach of the Classcraft gamification app, the learning approach of students was evaluated based on the behaviorism learning theory. The assessment of the gamification app was based on two criteria: Game elements of the gamification app and the Evaluation criteria in student learning (self- assessment and instructor's assessment). The gamification app was introduced to students to evaluate the learning capacity and its effect to students.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132371688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the WebVR technology based on mobile Internet is becoming more and more mature, but there is relatively little research on P2P transmission of WebVR scene data between mobile web pages. For the past WebTorrent scheme, the concept of interest domain and avatar behavior grouping is not considered, but only pure P2P transmission problem is considered. On the one hand, a more scalable WebVR peer-to-peer transmission platform is implemented based on PeerJS, on the other hand, considering the behavioral characteristics of WebVR avatars, a WebVR interest domain partitioning method based on user attribute recommendation algorithm is proposed. Experimental results show that the proposed scheme has good results for WebVR peer-to-peer transmission.
{"title":"A User Attribute Recommendation Algorithm and Peer3D Technology based WebVR P2P Transmission Scheme","authors":"Huijuan Zhang, Lei Qiao, Dongqing Wang","doi":"10.1145/3316615.3316726","DOIUrl":"https://doi.org/10.1145/3316615.3316726","url":null,"abstract":"At present, the WebVR technology based on mobile Internet is becoming more and more mature, but there is relatively little research on P2P transmission of WebVR scene data between mobile web pages. For the past WebTorrent scheme, the concept of interest domain and avatar behavior grouping is not considered, but only pure P2P transmission problem is considered. On the one hand, a more scalable WebVR peer-to-peer transmission platform is implemented based on PeerJS, on the other hand, considering the behavioral characteristics of WebVR avatars, a WebVR interest domain partitioning method based on user attribute recommendation algorithm is proposed. Experimental results show that the proposed scheme has good results for WebVR peer-to-peer transmission.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of society, applying data mining schemes in the chemometrics discipline is increasing rapidly which makes this field very popular. However, machine learning algorithms face challenges in selecting best algorithm's parameters as well as selecting the features of the data that affect the decision-making process. Limited studies have in-depth explored ways of enhancing decision support systems in the chemometrics domain. Therefore, this study aims at reinforcing the decision-making process through proposing a robust approach: "feature selection" and "algorithm optimization" in conjunction with "cross-validation". Precisely, stratified tenfold cross-validation method was utilized to evaluate the parameter selection of both Multilayer perceptron and Partial least-squares regression algorithms, from the one hand, and to select the best prediction features, from the other hand. Results exhibited that Multilayer perceptron model overperformed partial least-squares regression model. This confirms that Multilayer perceptron can be efficiently used in the chemometrics discipline. Our result also listed the selected feature for the utilized data. Consequently, current study opens the door for enhancing the industry, generally, and the chemometrics-related manufacturing, especially. It also sheds some light on the significance of adopting cross-validation for model selection and parameter optimization in the chemometrics domain for improving the quality of the decision-making process.
{"title":"Reinforcing the Decision-making Process in Chemometrics: Feature Selection and Algorithm Optimization","authors":"Samer Muthana Sarsam","doi":"10.1145/3316615.3316644","DOIUrl":"https://doi.org/10.1145/3316615.3316644","url":null,"abstract":"With the development of society, applying data mining schemes in the chemometrics discipline is increasing rapidly which makes this field very popular. However, machine learning algorithms face challenges in selecting best algorithm's parameters as well as selecting the features of the data that affect the decision-making process. Limited studies have in-depth explored ways of enhancing decision support systems in the chemometrics domain. Therefore, this study aims at reinforcing the decision-making process through proposing a robust approach: \"feature selection\" and \"algorithm optimization\" in conjunction with \"cross-validation\". Precisely, stratified tenfold cross-validation method was utilized to evaluate the parameter selection of both Multilayer perceptron and Partial least-squares regression algorithms, from the one hand, and to select the best prediction features, from the other hand. Results exhibited that Multilayer perceptron model overperformed partial least-squares regression model. This confirms that Multilayer perceptron can be efficiently used in the chemometrics discipline. Our result also listed the selected feature for the utilized data. Consequently, current study opens the door for enhancing the industry, generally, and the chemometrics-related manufacturing, especially. It also sheds some light on the significance of adopting cross-validation for model selection and parameter optimization in the chemometrics domain for improving the quality of the decision-making process.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123383522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaiful Bakhtiar bin Rodzman, Siti Suhaima binti Suhaili, N. K. Ismail, Nurazzah Abd Rahman, S. A. Aljunid, Aslida binti Omar
The complaint from users is an effective method to identify the quality of services and facilities provided by an organization. The efficiency to respond to users' complaint also depends on an effective workflow. By having an effective method and workflow, the action taken by the management to improve the quality of services and facilities can be done immediately and effectively. One of the ways is by classifying the complaints that will isolate related complaints. This paper presents the implementation of the classification system that combines the application of Complaint Concept Ontologies in Malay language as classifier rules with the BM25 model of Information Retrieval system. Experiments showed the semantic based elements such as Malay ontology may bring the improvement of the classification of the Malay Complaint. The result yielded showed that the proposed classifier produced better result in four category compared to BM25 original score that only produced better result in one category. OBMCS also outperformed the LDA model in all eight categories on the Recall, Precision and F-measure metrics. The finding proven the proposed system is very useful, especially to the Malay complaint in regards of classification for documents in the domain area.
{"title":"Domain Specific Classification of Malay Based Complaints using the Complaint Concept Ontologies","authors":"Shaiful Bakhtiar bin Rodzman, Siti Suhaima binti Suhaili, N. K. Ismail, Nurazzah Abd Rahman, S. A. Aljunid, Aslida binti Omar","doi":"10.1145/3316615.3316682","DOIUrl":"https://doi.org/10.1145/3316615.3316682","url":null,"abstract":"The complaint from users is an effective method to identify the quality of services and facilities provided by an organization. The efficiency to respond to users' complaint also depends on an effective workflow. By having an effective method and workflow, the action taken by the management to improve the quality of services and facilities can be done immediately and effectively. One of the ways is by classifying the complaints that will isolate related complaints. This paper presents the implementation of the classification system that combines the application of Complaint Concept Ontologies in Malay language as classifier rules with the BM25 model of Information Retrieval system. Experiments showed the semantic based elements such as Malay ontology may bring the improvement of the classification of the Malay Complaint. The result yielded showed that the proposed classifier produced better result in four category compared to BM25 original score that only produced better result in one category. OBMCS also outperformed the LDA model in all eight categories on the Recall, Precision and F-measure metrics. The finding proven the proposed system is very useful, especially to the Malay complaint in regards of classification for documents in the domain area.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122359053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A revolution in computational methods and statistics to process and analyse data into insight and knowledge is along with the growth of data. The paradigm of data analytic is changed from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output (predictive analytics). In the cycle of predictive analytics, data preparation is a very important stage. The main challenge faced is that raw data cannot be directly used for analysis and related to the quality of the data. Completeness is arising related to data quality. Missing data is one that often causes data to become incomplete. As a result, predictive analytics generated from these data becomes inaccurate. In this paper, the issues related to the missing data in predictive analytics will be discussed through a literature study from related research. Also, the challenges and direction that might occur in the predictive analytics domain with problems related to missing data will be presented.
{"title":"Missing Data Problem in Predictive Analytics","authors":"Heru Nugroho, K. Surendro","doi":"10.1145/3316615.3316730","DOIUrl":"https://doi.org/10.1145/3316615.3316730","url":null,"abstract":"A revolution in computational methods and statistics to process and analyse data into insight and knowledge is along with the growth of data. The paradigm of data analytic is changed from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output (predictive analytics). In the cycle of predictive analytics, data preparation is a very important stage. The main challenge faced is that raw data cannot be directly used for analysis and related to the quality of the data. Completeness is arising related to data quality. Missing data is one that often causes data to become incomplete. As a result, predictive analytics generated from these data becomes inaccurate. In this paper, the issues related to the missing data in predictive analytics will be discussed through a literature study from related research. Also, the challenges and direction that might occur in the predictive analytics domain with problems related to missing data will be presented.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"105 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122393941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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