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Fast kernel extreme learning machine for ordinal regression

knowledge-BASED SYSTEMS 2019年 177卷 44-54页

作者： Shi, Yong Li, Peijia Yuan, Hao Miao, Jianyu Niu, Lingfeng Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68118 USA Ningbo Dev Planning Res Inst Ningbo 315040 Zhejiang Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China

Ordinal regression is a special kind of machine learning problem, which aims to label patterns with an ordinal scale. Due to the ubiquitous existence of the ordering information in many practical cases, ordinal regression has received much attention and can be found in a great variety of applications. Meanwhile, Kernel Extreme Learning Machine (KELM), as the extension of Extreme Learning Machine in the framework of kernel learning, has shown its strength in many machine learning tasks. Nevertheless, existing KELM methods have paid little attention to ordinal regression especially in large-scale situations. In this paper, we consider the kernel technique and ordinal scales in labels at the same time, and propose a new KELM model for ordinal regression by exploiting a quadratic cost-sensitive encoding scheme. In order to make the training process more efficient in the big data scenario, a fast algorithm is designed based on the low rank approximation. The incomplete Cholesky factorization and the Sherman-Morrison-Woodbury formula are utilized together to avoid computing the inverse of the kernel matrix. The time complexity of the new algorithm is thus linear with the number of the training instances, which makes it suitable to the large-scale occasions. Numerical experiments on multiple public datasets validate the effectiveness and efficiency of the proposed methods. (C) 2019 Published by Elsevier B.V.

关键词： Ordinal regression Kernel Extreme learning machine Low-rank approximation

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Alzheimer's Disease Diagnosis Using Enhanced Inception Network Based on Brain Magnetic Resonance Image

Alzheimer's Disease Diagnosis Using Enhanced Inception Netwo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Cui, Zhenyu Gao, Zhiao Leng, Jiaxu Zhang, Tianlin Quan, Pei Zhao, Wei Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China

ISBN: (纸本)9781728118673

An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease(AD). Nowadays, Alzheimer's disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimer's disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimer's disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimer's disease and normal, and the chance of it turning into Alzheimer's disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimer's disease accurately, it will be beneficial for the early diagnosis of Alzheimer's disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimer's disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.

关键词： Alzheimer's disease diagnosis magnetic resonance image neural network

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Diffusion network embedding

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PATTERN RECOGNITION 2019年 88卷 518-531页

作者： Shi, Yong Lei, Minglong Yang, Hong Niu, Lingfeng Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Technol Sydney Ctr Artificial Intelligence Sydney NSW 2000 Australia

In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes. Second, in highly biased networks, random walks are likely to bias to high-degree nodes and neglect the global structure information. To solve the limitations, we present in this paper a network diffusion embedding method. To solve the first limitation, our method uses a diffusion driven process to capture both depth and breadth information in networks. Temporal information is also included into node sequences to strengthen information preserving. To solve the second limitation, our method uses the network inference method based on information diffusion cascades to capture the global network information. Experiments show that the new proposed method is more robust to highly unbalanced networks and well performed when sampling under each node is rare. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Network embedding Cascades Diffusion process Network inference Dimension reduction

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HANP-Miner: High average utility nonoverlapping sequential pattern mining

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knowledge-BASED SYSTEMS 2021年 229卷

作者： Wu, Youxi Geng, Meng Li, Yan Guo, Lei Li, Zhao Fournier-Viger, Philippe Zhu, Xingquan Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hebei Data Driven Ind Intelligent Engn Res Ctr Tianjin 300401 Peoples R China Alibaba Grp Hangzhou 310000 Zhejiang Peoples R China Harbin Inst Technol Shenzhen Sch Humanities & Social Sci Shenzhen Peoples R China Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing 100084 Peoples R China

Nonoverlapping sequential pattern mining (SPM) is a data analysis task, which aims at identifying repetitive sequential patterns with gap constraint in a set of discrete sequences. Nonoverlapping means that any character in the sequence can be rematched by characters at different positions in the pattern, while overlapping means that any character can be reused at the same position, which can be regarded as no condition. Compared with overlapping SPM, nonoverlapping SPM has more strict constraint on the occurrences of pattern, and meets the Apriori property. However, current algorithms mine frequent or closed patterns, resulting in some low-frequency but extremely important patterns being ignored. To tackle this issue, this paper proposes to mine high average utility nonoverlapping sequential patterns (HANP). An efficient algorithm called HANP-Miner is proposed, which involves two key steps: support calculation and candidate pattern reduction. To calculate the support (the occurrence frequency of a pattern), depth-first search and backtracking strategies based on the simplified Nettree structure are adopted, an approach that effectively reduces the time and space complexities of the algorithm. To effectively reduce the number of candidate patterns, a pattern join strategy based on an upper bound on the average utility is proposed. Experiments on biological sequences and real sales sequences are carried out to verify the efficiency of HANP-Miner and the superiority of HANPs. The results demonstrate that HANP-Miner is not only more efficient, but that the HANPs mined in this way are also more valuable than existing frequent patterns. The algorithms and datasets can be downloaded from https://***/wuc567/Pattern-mining/tree/master/HANP-Miner. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Sequential pattern mining High average utility Depth-first search Pattern join

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Threshold and Deadline Effect in Crowdfunding 7

Threshold and Deadline Effect in Crowdfunding

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7th International Conference on Information Technology and Quantitative management (ITQM) - Information Technology and Quantitative management Based on Artificial Intelligence

作者： Song, Yan Tian, Xin Luo, Chunlin Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China

The deadline effect and threshold effect are common in product development and group buying. We consider these two effects also exist in crowdfunding, affecting the crowdfunding performance in the future. We conduct an empirical study using a dataset from *** to examine these two effects and their interactive relationship on the following crowdfunding performance. We find the threshold effect exists in crowdfunding and has a positive influence on the following contribution behaviour. The deadline effect also can stimulate the crowdfunding behaviour via limited fundraising time. Furthermore, we find the threshold effect and deadline effect are complements that increase the amount pledged. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： crowdfunding performance threshold effect deadline effect interactive effect

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Joint Sparse Regularization for Dictionary Learning

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COGNITIVE COMPUTATION 2019年第5期11卷 697-710页

作者： Miao, Jianyu Cao, Heling Jin, Xiao-Bo Ma, Rongrong Fei, Xuan Niu, Lingfeng Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

As a powerful data representation framework, dictionary learning has emerged in many domains, including machine learning, signal processing, and statistics. Most existing dictionary learning methods use the l(0) or l(1) norm as regularization to promote sparsity, which neglects the redundant information in dictionary. In this paper, a class of joint sparse regularization is introduced to dictionary learning, leading to a compact dictionary. Unlike previous works which obtain sparse representations independently, we consider all representations in dictionary simultaneously. An efficient iterative solver based on ConCave-Convex Procedure (CCCP) framework and Lagrangian dual is developed to tackle the resulting model. Further, based on the dictionary learning with joint sparse regularization, we consider the multi-layer structure, which can extract the more abstract representation of data. Numerical experiments are conducted on several publicly available datasets. The experimental results demonstrate the effectiveness of joint sparse regularization for dictionary learning.

关键词： Dictionary learning Joint sparse regularization Multi-layer structure

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Predicting Chronic Diseases Based on Split and Merge Method and Streaming Feature Causal Structure Learning Algorithm 5

Predicting Chronic Diseases Based on Split and Merge Method ...

引用

5th International Conference on Fuzzy Systems and data mining (FSDM)

作者： Lu, Sensen Yang, Jing Chen, Yu Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei Anhui Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9781643680194;9781643680187

Early detections of chronic diseases contribute to the prevention of such diseases. Due to the limitation of dealing with big data of BN structure learning, we figure out a new structure learning algorithm called SAM (Split And Merge) algorithm based on the thought of Adaboost, to process big data. We combine SAM algorithm with casual discovery based on the streaming features(CD-SF) algorithm to form the SAM-CD-SF algorithm. To evaluate the performance of the proposed approach, we conducted extensive experiments on the questionnaires collected from Behavior Risk Factor Surveillance System. The SAM-CD-SF can effectively deal with relative large datasets. In order to further improve the time performance, we combine SAM algorithm with casual discovery with symmetrical uncertainty based on the streaming features (CD-SU-SF) to form SAM-CD-SU-SF algorithm. Compared with SAM-CD-SF algorithm, SAM-CD-SU-SF algorithm have slightly worse accuracy, but much better time performance.

关键词： Chronic Diseases Streaming Feature Structure Learning Split and Merge

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The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

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JOURNAL OF FORECASTING 2020年第7期39卷 1166-1178页

作者： Jin, Zhenni Guo, Kun Sun, Yi Lai, Lin Liao, Zhewen Univ Chinese Acad Sci Sino Danish Coll Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Peking Univ HSBC Business Sch Shenzhen Peoples R China

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.

关键词： industries asymmetry investor sentiment stock price prediction SVR

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Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection

arXiv

引用

arXiv 2021年

作者： Li, Kai Yang, Jie Ma, Siwei Wang, Bo Wang, Shanshe Tian, Yingjie Qi, Zhiquan School of Computer Science Institute of Digital Media Peking University Beijing100871 China School of Mathematics Sciences University of Chinese Academy of Sciences Beijing100049 China Advanced Institute of Information Technology Peking University Hangzhou310005 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation University of Chinese Academy of Sciences No. 3 South Yitiao Zhongguancun Haidian District Beijing100190 China

Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good performance, but ignore detection efficiency in practical applications. In this article, to address the first issue, we collected two new databases (i.e. Rain365 and Sun520) captured in rainy and sunny days respectively, which enrich the data of the open source community. For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet. Specifically, we introduce a novel olive-shaped structure for the encoder network, a light-weight multi-scale block and a new up-sampling method in the decoder network. Numerous experiments show that our model can better balance detection performance and efficiency compared with previous models. Especially, on the Sun520 dataset, our CarNet significantly advances the state-of-the-art performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an improved detection speed (104 frame per second) which is orders of magnitude faster than some recent DCNNs-based algorithms specially designed for crack detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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A pyramid scheme model based on "consumer rebate" frauds

arXiv

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arXiv 2019年

作者： Shi, Yong Li, Bo Long, Wen School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

There are various types of pyramid schemes which have inflicted or are inflicting losses on many people in the world. We propose a pyramid scheme model which has the principal characters of many pyramid schemes appeared in recent years: promising high returns, rewarding the participants recruiting the next generation of participants, and the organizer will take all the money away when he finds the money from the new participants is not enough to pay the previous participants interest and rewards. We assume the pyramid scheme carries on in the tree network, ER random network, SW small-world network or BA scale-free network respectively, then give the analytical results of how many generations the pyramid scheme can last in these cases. We also use our model to analyse a pyramid scheme in the real world and we find the connections between participants in the pyramid scheme may constitute a SW small-world network. Copyright © 2019, The Authors. All rights reserved.

关键词： Small-world networks

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