With the rapid development of information technology and the widespread application of information, social networks are becoming more convenient and faster tools for information release and acquisition. Predicting topic popularity is important for online referral systems, marketing services and public opinion controls. In this paper, we predict the popularity of topics with the help of time series analysis methods, verifying the validity of ARMA model in topic popularity prediction.
{"title":"Topics' popularity prediction based on ARMA model","authors":"Yichen Song, Aiping Li, Yong Quan","doi":"10.1145/3208788.3208799","DOIUrl":"https://doi.org/10.1145/3208788.3208799","url":null,"abstract":"With the rapid development of information technology and the widespread application of information, social networks are becoming more convenient and faster tools for information release and acquisition. Predicting topic popularity is important for online referral systems, marketing services and public opinion controls. In this paper, we predict the popularity of topics with the help of time series analysis methods, verifying the validity of ARMA model in topic popularity prediction.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129342317","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}
Restricted by the shallow structure of Extreme Learning Machine(ELM), the ideal fitting effect can not be achieved even if large hidden nodes are set. In order to obtain better feature representation and classification performance, this paper proposes a Hybrid Hierarchical Extreme Learning Machine (HH-ELM) on the hierarchical thought of Hierarchical Extreme Learning Machine(H-ELM). The feature extraction part uses ELM-Based Auto-Encoder(ELM-AE) based on L1-norm regularization to optimize the hidden layer weights, and the classification part adopts Improved Tow-hidden-layer Extreme Learning Machine(ITELM). Experimental results on UCI datasets and Mnist images datasets show that HH-ELM has better classification results and robustness.
{"title":"Hybrid hierarchical extreme learning machine","authors":"Meiyi Li, Changfei Wang, Qingshuai Sun","doi":"10.1145/3208788.3208793","DOIUrl":"https://doi.org/10.1145/3208788.3208793","url":null,"abstract":"Restricted by the shallow structure of Extreme Learning Machine(ELM), the ideal fitting effect can not be achieved even if large hidden nodes are set. In order to obtain better feature representation and classification performance, this paper proposes a Hybrid Hierarchical Extreme Learning Machine (HH-ELM) on the hierarchical thought of Hierarchical Extreme Learning Machine(H-ELM). The feature extraction part uses ELM-Based Auto-Encoder(ELM-AE) based on L1-norm regularization to optimize the hidden layer weights, and the classification part adopts Improved Tow-hidden-layer Extreme Learning Machine(ITELM). Experimental results on UCI datasets and Mnist images datasets show that HH-ELM has better classification results and robustness.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"35 35","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134501333","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}
Multiple arm bandit algorithm is widely used in gaming, gambling, policy generation, and artificial intelligence projects and gets more attention recently. In this paper, we explore non-stationary reward MAB problem with limited query budget. An upper confidence bound (UCB) based algorithm for the discounted MAB budget finite problem, which uses reward-cost ratio instead of arm rewards in discount empirical average. In order to estimate the instantaneous expected reward-cost ratio, the DUCB-BF policy averages past rewards with a discount factor giving more weight to recent observations. Theoretical regret bound is established with proof to be over-performed than other MAB algorithms. A real application on maintenance recovery models refinement is explored. Results comparison on 4 different MAB algorithms and DUCB-BF algorithm yields lowest regret as expected.
{"title":"Multi-model optimization with discounted reward and budget constraint","authors":"Jixuan Shi, Mei Chen","doi":"10.1145/3208788.3208796","DOIUrl":"https://doi.org/10.1145/3208788.3208796","url":null,"abstract":"Multiple arm bandit algorithm is widely used in gaming, gambling, policy generation, and artificial intelligence projects and gets more attention recently. In this paper, we explore non-stationary reward MAB problem with limited query budget. An upper confidence bound (UCB) based algorithm for the discounted MAB budget finite problem, which uses reward-cost ratio instead of arm rewards in discount empirical average. In order to estimate the instantaneous expected reward-cost ratio, the DUCB-BF policy averages past rewards with a discount factor giving more weight to recent observations. Theoretical regret bound is established with proof to be over-performed than other MAB algorithms. A real application on maintenance recovery models refinement is explored. Results comparison on 4 different MAB algorithms and DUCB-BF algorithm yields lowest regret as expected.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132708319","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}
Nanzhi Wang, Lin Li, Linlong Xiao, Guocai Yang, Yue Zhou
With the wide spreading of network and capital inflows, Electronic Sport (ES) is developing rapidly in recent years and has become a competitive sport that cannot be ignored. Compared with traditional sports, the data of this industry is large in size and has the characteristics of easy-accessing and normalization. Based on these, data mining and machine learning methods can be applied to improve players' skills and help players make strategies. In this paper, a new approach predicting the outcome of an electronic sport DOTA2 was proposed. In earlier studies, the heroes' draft of a team was represented by unit vectors or its evolution, so the complex interactions among heroes were not captured. In our approach, the outcome prediction was performed in two steps. In the first step, Heroes in DOTA2 were quantified from 17 aspects in a more accurate way. In the second step, we proposed a new method to represent a heroes' draft. A priority table of 113 heroes was created based on the prior knowledge to support this method. The evaluation indexes of several machine learning methods on this task have been compared and analyzed in this paper. Experimental results demonstrate that our method was more effective and accurate than previous methods.
{"title":"Outcome prediction of DOTA2 using machine learning methods","authors":"Nanzhi Wang, Lin Li, Linlong Xiao, Guocai Yang, Yue Zhou","doi":"10.1145/3208788.3208800","DOIUrl":"https://doi.org/10.1145/3208788.3208800","url":null,"abstract":"With the wide spreading of network and capital inflows, Electronic Sport (ES) is developing rapidly in recent years and has become a competitive sport that cannot be ignored. Compared with traditional sports, the data of this industry is large in size and has the characteristics of easy-accessing and normalization. Based on these, data mining and machine learning methods can be applied to improve players' skills and help players make strategies. In this paper, a new approach predicting the outcome of an electronic sport DOTA2 was proposed. In earlier studies, the heroes' draft of a team was represented by unit vectors or its evolution, so the complex interactions among heroes were not captured. In our approach, the outcome prediction was performed in two steps. In the first step, Heroes in DOTA2 were quantified from 17 aspects in a more accurate way. In the second step, we proposed a new method to represent a heroes' draft. A priority table of 113 heroes was created based on the prior knowledge to support this method. The evaluation indexes of several machine learning methods on this task have been compared and analyzed in this paper. Experimental results demonstrate that our method was more effective and accurate than previous methods.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133281762","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}
It is often the case that datasets are imbalanced in the real world. In this situation, it is minimizing misclassification costs rather than classification accuracy that is the primary goal of classification algorithms. To tackle this problem and improve the performance of classifiers, sampling is widely employed. In this paper, we propose a new diversity-based under-sampling technique for class-imbalanced datasets. The key idea is to balance a data set by choosing only the potential informative samples of the majority class according to diversity of class probability calculation. The experimental results on 5 class-imbalanced datasets show that our method performs better than two existing sampling techniques in terms of total misclassification costs.
{"title":"A diversity-based method for class-imbalanced cost-sensitive learning","authors":"S. Dong, Yongcheng Wu","doi":"10.1145/3208788.3208792","DOIUrl":"https://doi.org/10.1145/3208788.3208792","url":null,"abstract":"It is often the case that datasets are imbalanced in the real world. In this situation, it is minimizing misclassification costs rather than classification accuracy that is the primary goal of classification algorithms. To tackle this problem and improve the performance of classifiers, sampling is widely employed. In this paper, we propose a new diversity-based under-sampling technique for class-imbalanced datasets. The key idea is to balance a data set by choosing only the potential informative samples of the majority class according to diversity of class probability calculation. The experimental results on 5 class-imbalanced datasets show that our method performs better than two existing sampling techniques in terms of total misclassification costs.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756515","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 rapid development of emotion recognition, emotion recognition based on EEG signals and physiological signals has drawn much attention from researchers. However, due to the consistency of multi-source information in emotional expression, emotion recognition based on single modal information is still unsatisfactory. Therefore, we proposed a feature fusion algorithm based on Discriminative Canonical correlation analysis, two modes are dealt with simultaneously, the correlation between the two classes of samples is taken as a similarity measure, introduced the class information of the sample, Fully consider the correlation between similar samples and the correlation between different samples. We use the DEAP database and use the DCCA method to fuse the physiological signals and the EEG signals, which greatly improves the classification effect. The classification of liking dimension is 68.21%, which is about 10% higher than other methods and about 2% higher than the CCA model.
{"title":"Research on feature fusion for emotion recognition based on discriminative canonical correlation analysis","authors":"Chuqi Liu, C. Li, Ziping Zhao","doi":"10.1145/3208788.3208804","DOIUrl":"https://doi.org/10.1145/3208788.3208804","url":null,"abstract":"With the rapid development of emotion recognition, emotion recognition based on EEG signals and physiological signals has drawn much attention from researchers. However, due to the consistency of multi-source information in emotional expression, emotion recognition based on single modal information is still unsatisfactory. Therefore, we proposed a feature fusion algorithm based on Discriminative Canonical correlation analysis, two modes are dealt with simultaneously, the correlation between the two classes of samples is taken as a similarity measure, introduced the class information of the sample, Fully consider the correlation between similar samples and the correlation between different samples. We use the DEAP database and use the DCCA method to fuse the physiological signals and the EEG signals, which greatly improves the classification effect. The classification of liking dimension is 68.21%, which is about 10% higher than other methods and about 2% higher than the CCA model.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873426","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 study the existence of solutions to a class fuzzy differential equations subject to the special two-point boundary value problems for fuzzy differential equations from the point of view of generalized differentiability. Using the switching point, it could divide two initial value problem of fuzzy differential equations, by adding some conditions, obtains the solutions of a certain type of two-point boundary value fuzzy problem exists and some examples illustrate the effectiveness of the proposed approach.
{"title":"Two-point boundary value problems for fuzzy differential equations under generalized differentiability","authors":"Liu Qian, Yan Junna","doi":"10.1145/3208788.3208791","DOIUrl":"https://doi.org/10.1145/3208788.3208791","url":null,"abstract":"This paper study the existence of solutions to a class fuzzy differential equations subject to the special two-point boundary value problems for fuzzy differential equations from the point of view of generalized differentiability. Using the switching point, it could divide two initial value problem of fuzzy differential equations, by adding some conditions, obtains the solutions of a certain type of two-point boundary value fuzzy problem exists and some examples illustrate the effectiveness of the proposed approach.","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113949372","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}
{"title":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","authors":"","doi":"10.1145/3208788","DOIUrl":"https://doi.org/10.1145/3208788","url":null,"abstract":"","PeriodicalId":211585,"journal":{"name":"Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124710186","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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