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Clustering ensembles algorithm based on fractal dimension

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 2012年第SUPPL. 1期42卷 364-367页

作者： Wu, Xiao-Xuan Ni, Zhi-Wei Ni, Li-Ping School of Management and Business Intelligence Institute Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology Hefei 230009 China

The fractal dimension based clustering ensembles algorithm was studied. It introduced clustering algorithm based on fractal dimension at first in order to create partitions for clustering ensembles, then using voting strategy to get ensembles result. Finally, an idea on distributed clustering ensembles under cloud computing environment was brief discussed. Fractal dimension based clustering ensembles algorithm can offer better solutions in terms of robustness, novelty and stability than the single clustering algorithm based on fractal dimension. Combining the approaches based on grid and fractal, the clustering algorithm called grid and fractal dimension based clustering algorithm (GFDC) was presented to create partitions for clustering ensembles instead of clustering algorithm based on fractal dimension. GFDC is able to capture arbitrary shapes and non-neighboring clustering and can be applied to the massive and high-dimension dataset.

关键词： Fractal dimension

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Probabilistic Runoff Forecasting Considering Stepwise Decomposition Framework and External Feature Integration Structure

SSRN

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SSRN 2023年

作者： Cao, Chaojin He, Yaoyao Cai, Siyu School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Ministry of Education Hefei230009 China Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development Hefei University of Technology Hefei230009 China China Institute of Water Resources and Hydropower Research Beijing100038 China

As dramatic weather changes complexify the change of runoff and introduce significant uncertainties, probabilistic runoff forecasting becomes increasingly important for water resource managers because it can quantify the uncertainty in the prediction process. However, little research has been conducted on how to employ decomposition techniques rationally in probabilistic prediction while effectively considering external factors. To fill the gap, this paper proposes a novel framework for probabilistic runoff forecasting, which incorporates several key components. It first utilizes the stepwise decomposition (SD) technique to prevent the information leakage problem. Then an external feature integration structure is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction technique is implemented to mitigate the boundary distortion problem. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM). This method is referred to as STL-LASSO-QRLSTM. We employ two datasets from the Lutaizi and Wangjiaba stations in China to conduct case studies. Several hybrid models and comparison frameworks are developed to conduct adequate comparative experiments, which are evaluated using six metrics that measure deterministic and probabilistic performance. The findings show that the predictive power of the proposed model exceeds comparative methods based on the normal and SD frameworks. This research holds immense importance for developing probabilistic models based on deep learning for runoff forecasting. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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An Empirical Study on Personality Characteristics of College Volunteers: Based on the Collected Data from Shanghai Expo

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Procedia - Social and Behavioral Sciences 2013年 83卷 969-974页

作者： Ya-di Gu Dong-xiao Gu Qing Wei Shen Pan Wen-en Chen Yi Ge Students’ Affairs Division at University of Shanghai for Science and Technology Shanghai200093 P.R. CHINA School of Management at Hefei University of Technology Hefei Anhui230009 P.R. CHINA School of Foreign Studies at Hefei University of Technology Hefei Anhui230009 P.R. CHINA Engineering Research Center of Intelligent Decision-Making and Information System Technology of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education of China Hefei Anhui 230009 P.R. CHINA Department of Electronic Information Technology at Tianjin Normal University Tianjin 300387 P.R. CHINA

Objective : This study is to investigate the personality characteristics of student volunteers, which would provide a reference to select and train qualified volunteers for the future. Methods : 233 student volunteers and 236 non-volunteers are selected for our study. Based on the data of 16PF personality exported from the database, statistical software SPSS15.0 is used to conduct data analysis. Result : Volunteers and non-volunteers have significant differences of personality: the 16 basic personality factors, the gregariousness, excitement, daring and independence scores are significantly different; in the other eight binary factors, emotional and serene in the alert type, introverted and extroverted type, timid and bold type factors have significantly different scores. Male and female volunteers have significant personality difference in some respects. Conclusion : College student volunteers have obvious personality traits of extroversion, optimism, cheerfulness, enthusiasm, self-confidence etc. In addition, male and female volunteers have gender differences in personality characteristics in some aspects.

关键词： College students Volunteers Personality traits Shanghai Expo Selection

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC

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IOP Conference Series: Earth and Environmental Science 2020年第5期474卷

作者： Lin Liu Yanyun Lu Shuqin Li School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power.

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Understanding Continued Use Intention of AI Assistants

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Data processor for Better Business Education 2023年第6期63卷 1424页

作者： Yuguang Xie Shuping Zhao Peiyu Zhou Changyong Liang a Hefei University of Technology Hefei China a Hefei University of Technology Hefei Chinab Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education Hefei China

ABSTRACTIn recent years, smart home assistants have been used by a large number of people due to their simple, hands-free, voice-based operation. To ensure the long-term success and widespread dissemination of a product, it is important to evaluate its continued use. This study is mainly based on uses and gratifications theory to explore the relationship between the initial use of, gratification provided by, and continued use intention of smart home assistants, and to analyze differences in use by different age groups. The results confirm that different types of SHAs use lead to different levels of gratification in different categories. And gratification of different categories of users has a significant positive impact on the continued use intention. In addition, significant differences exist in the impact path of using smart home assistants to alleviate loneliness, among different age groups.

关键词： Human-computer interaction smart home assistants continued use intention U&G theory age differences

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

arXiv

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arXiv 2024年

作者： Jing, Erkang Liu, Yezheng Chai, Yidong Yu, Shuo Liu, Longshun Jiang, Yuanchun Wang, Yang School of Management Hefei University of Technology Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei China Rawls College of Business Texas Tech University LubbockTX United States School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks

arXiv

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arXiv 2024年

作者： Liu, Yi Tang, Xiaoan Pedrycz, Witold Zhang, Qiang The Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education The School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey

Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applications usually have high requirements on the balance between time complexity and accuracy of influential nodes identification. To address the challenges of imperfect node influence measurement and inefficient seed nodes selection mechanism in such class of foregoing techniques, this article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence maximization in complex networks. First, we develop a method for measuring the importance of different nodes in networks based on an original concept of Dual-Scale Community-Hierarchy Information that synthesizes both hierarchy structural information and community structural information of nodes. The community structural information contained in the nodes is measured by a new notion of Hierarchical-Community Entropy. Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection. Hereinto, a low-computational-cost mutual voting mechanism and an updating strategy called Lazy Score Updating Strategy are newly constructed for optimizing the selecting of seed nodes. Third, we develop a balance index to evaluate the performance of different methods in striking the tradeoff between time complexity and the accuracy of influential nodes identification. Finally, we demonstrate the approach performance over ten public datasets. The extensive experiments show that the proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification. Compared with the method with the second highest value of the balance index, our approach can be improved by at most 9.29%. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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Network analysis of R&D technology spillovers effect in the Yangtze River Delta Region

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IOP Conference Series: Earth and Environmental Science 2020年第1期615卷

作者： Guili Jiang Jianling Jiao Jingjing Li Chaoping Sun Tourism College of Zhejiang Hangzhou Zhejiang China School of Management Hefei University of Technology Hefei Anhui China Key Laboratory of Process Optimization and Intelligent Decision-making (Hefei University of Technology) Ministry of Education Hefei Anhui 230009 China

This article evaluates the evolution of inter-city R&D technology spillover in the Yangtze River Delta Region (YRDR) using social network analysis method. Empirical results indicate that the inter-city R&D technology spillovers are sparse, but gradually increasing. Four characteristic cohesive subgroups are formed, subgroup I formed by core cities like Shanghai and Nanjing is the technical sender and the subgroup is closely connected. Nanjing's role as a network bridge has been replaced by Jiaxing.

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Independent or Social Driven decision? A Counterfactual Refinement Strategy for Graph-Based Social Recommendation

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ACM Transactions on Information Systems 1000年

作者： Dongyang Li Jianshan Sun Chongming Gao Fuli Feng Kun Yuan School of Management Hefei University of Technology China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education China University of Science and Technology of China Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) China University of Science and Technology of China China Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management China

Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the diverse nature of social relationships. Some individuals with independent personalities often prioritize their own interests over friends’ advice when making purchase decisions. Conversely, those who seek advice from others are more susceptible to social influence. Moreover, existing methods tend to overlook redundant and noisy social relationships within the network, hindering their ability to achieve accurate recommendations. In response, this paper proposes a novel counterfactual method to understand the causal factors driving purchase behaviors, thereby identifying the influence of users’ friends on their purchase decisions. By answering counterfactual questions about the influence of a friend's purchase behavior on the user's choices, we develop a causal model to represent social influence in the network. Our proposed refinement strategy, grounded in causal inference, generates counterfactual purchase behavior and guides the refinement of the social graph. Moreover, we present tailored graph refinement methods at various levels, ensuring fine-grained improvements. Experimental results on benchmark data demonstrate that the application of our strategy to different social recommendation models significantly enhances their predictive performance. The source code has been made available on https://***/LDY911/CFRSSR-Code.

关键词： social recommendation graph denoising causal inference

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

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ACM Transactions on Information Systems 1000年

作者： Erkang Jing Yezheng Liu Yidong Chai Shuo Yu Longshun Liu Yuanchun Jiang Yang Wang School of Management Hefei University of Technology China School of Management Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China School of Management Hefei University of Technology and Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management China Rawls College of Business Texas Tech University U.S. School of Computer Science and Information Engineering Hefei University of Technology China

关键词： Personalized music recommendation emotion heterogeneity music mood preference heterogeneity deep learning generative model

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