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Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

COMPLEXITY 2020年第1期2020卷

作者： Wu, Wenshuai Xu, Zeshui Kou, Gang Shi, Yong Sichuan Univ Business Sch Chengdu 610065 Sichuan Peoples R China SouthWestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Sichuan Peoples R China Chinese Acad Sci CAS Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making support for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.

关键词： Clustering algorithms

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Uncertain Statistical Inference Models With Imprecise Observations

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2018年第2期26卷 409-415页

作者： Yao, Kai Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

The observations of some samples from the population of a probability density function with unknown parameters are usually imprecise due to various reasons. By employing uncertain variables to model these imprecise observations, this paper proposes an interdiscipline called the uncertain statistical inference, which is composed of statistical inference and uncertainty theory. It presents three types of statistic inference problems with imprecise observations that are the point estimation, the hypothesis test, and the interval estimation. Then, it proposes a method of moments and a method of likelihood function (maximum likelihood estimation) for the first problem, and a method of likelihood ratio function for the other two problems.

关键词： Hypothesis test interval estimation point estimation uncertain variable

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Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based data Collection Method

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2019年第5期18卷 1605-1629页

作者： Dong, Jichang Dai, Wei Liu, Ying Yu, Lean Wang, Jie Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Beijing 100190 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA

In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (***) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy.

关键词： Learning-based collection method search engine data financial time series forecasting stock market prices

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CenterNet: keypoint Triplets for Object Detection 17

CenterNet: Keypoint Triplets for Object Detection

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Duan, Kaiwen Bai, Song Xie, Lingxi Qi, Honggang Huang, Qingming Tian, Qi Univ Chinese Acad Sci Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728148038

In object detection, keypoint-based approaches often experience the drawback of a large number of incorrect object bounding boxes, arguably due to the lack of an additional assessment inside cropped regions. This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules, cascade corner pooling, and center pooling, that enrich information collected by both the top-left and bottom-right corners and provide more recognizable information from the central regions. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, outperforming all existing one-stage detectors by at least 4.9%. Furthermore, with a faster inference speed than the top-ranked two-stage detectors, CenterNet demonstrates a comparable performance to these detectors. Code is available at https://***/Duankaiwen/CenterNet.

关键词： Object detection

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Evolution of the Internet Architecture, from Network Model to Internet Brain Model 5

Evolution of the Internet Architecture, from Network Model t...

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

作者： Liu, Feng Liu, Ying Shi, Yong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

ISBN: (纸本)9781643680194;9781643680187

Based on the new features of the frontier science and technology in the 21st century, this paper proposes that the Internet has evolved from a network structure to a brain model in the past 50 years. The rise of technologies including Internet of Things, cloud computing, big data, mobile Internet, industrial Internet, edge computing, artificial intelligence, etc. are all related to this change in the Internet. This paper proposes the definition of a new Internet brain model, elaborates the causes for the emergence of a number of cutting-edge technologies and interrelationships thereof, by depicting the development process of the Internet brain, and predicts the new technologies that will emerge in the next 20 years.

关键词： Internet internet brain network

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Concatenated Attention Neural Network for Image Restoration

arXiv

引用

arXiv 2020年

作者： Tian, YingJie Wang, YiQi Yang, LinRui Qi, ZhiQuan School of Economics and Management University of Chinese Academy of Sciences Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science Technology University of Chinese Academy of Sciences Beijing China Sino-Danish College University of Chinese Academy of Sciences China

In this paper, we present a general framework for low-levelvision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this paper are as follows: First, by applying concise but effective concatenation and feature selection mechanism, we establish a novel connection mechanism which connect different modules in the modules stacking network. Second, both pixel-wise and channel-wise attention mechanisms are used in each module convolution layer, which promotes further extraction of more essential information in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Ensemble learning with label proportions for bankruptcy prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 146卷

作者： Chen, Zhensong Chen, Wei Shi, Yong Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China CAS Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA Southwest Minzu Univ Coll Elect & Informat Engn Chengdu 610041 Sichuan Peoples R China

Corporate bankruptcy prediction is an interesting and important research topic that can be conceived in many practical applications. Recently, machine learning based methods have been widely proposed to solve the problem of bankruptcy prediction. However, the existing models do not consider that large amounts of instance-level labeled training data are hard to be obtained in practice. In this paper, we propose to address bankruptcy prediction problem from the perspective of learning with label proportions, where the unlabeled training data are provided in different bags and only giving the bag-level proportion of instances belonging to a particular class. Then, we contribute two novel prediction methods, termed as Bagged-pSVM and Boosted-pSVM, based on proportion support vector machines and ensemble strategies including bagging and boosting. The proposed methods can not only explicitly model the unknown instance-level labels and the known label proportions under a large-margin framework, but also improve the performance through introducing ensemble learning strategies. Extensive experiments on the benchmark datasets demonstrate their efficiency and superiority on solving the problem of bankruptcy prediction. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Bankruptcy prediction Ensemble learning Learning with label proportion Proportion-SVM

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Tests for regression coefficients in high dimensional partially linear models

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STATISTICS & PROBABILITY LETTERS 2020年 163卷 108772页

作者： Liu, Yan Zhang, Sanguo Ma, Shuangge Zhang, Qingzhao Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Yale Univ Dept Biostat New Haven CT 06520 USA Xiamen Univ Sch Econ Wang Yanan Inst Studies Econ MOE Key Lab Econ & Fujian Key Lab Stat Xiamen Fujian Peoples R China

We propose a U-statistics test for regression coefficients in high dimensional partially linear models. In addition, the proposed method is extended to test part of the coefficients. Asymptotic distributions of the test statistics are established. Simulation studies demonstrate satisfactory finite-sample performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： High-dimensional analysis Partially linear models Regression coefficients U-statistics

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Feature weighted confidence to incorporate prior knowledge into support vector machines for classification

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knowledge AND INFORMATION SYSTEMS 2019年第2期58卷 371-397页

作者： Zhang, Wen Yu, Lean Yoshida, Taketoshi Wang, Qing Beijing Univ Technol Sch Econ & Management Beijing 100124 Peoples R China Beijing Univ Chem Technol Ctr Big Data Sci Beijing 100029 Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci 1-1 Ashahidai Nomi Ishikawa 9231292 Japan Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

This paper proposes an approach called feature weighted confidence with support vector machine (FWC-SVM) to incorporate prior knowledge into SVM with sample confidence. First, we use prior features to express prior knowledge. Second, FWC-SVM is biased to assign larger weights for prior weights in the slope vector than weights corresponding to non-prior features. Third, FWC-SVM employs an adaptive paradigm to update sample confidence and feature weights iteratively. We conduct extensive experiments to compare FWC-SVM with the state-of-the-art methods including standard SVM, WSVM, and WMSVM on an English dataset as Reuters-21578 text collection and a Chinese dataset as TanCorpV1.0 text collection. Experimental results demonstrate that in case of non-noisy data, FWC-SVM outperforms other methods when the retaining level is not larger than 0.8. In case of noisy data, FWC-SVM can produce better performance than WSVM on Reuters-21578 dataset when the retaining level is larger than 0.4 and on TanCorpV1.0 dataset when the retaining level is larger than 0.5. We also discuss the strength and weakness of the proposed FWC-SVM approach.

关键词： Feature weighted confidence Prior knowledge Support vector machine Classification

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A neural translating general hyperplane for knowledge graph embedding

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JOURNAL OF COMPUTATIONAL SCIENCE 2019年 30卷 108-117页

作者： Zhu, Qiannan Zhou, Xiaofei Zhang, Peng Shi, Yong Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Ant Financial Serv Grp Hangzhou Zhejiang Peoples R China CAS Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

For completing knowledge graph, many translation-based models, like that Trans(E and H) which embed a knowledge graph into a continuous vector space and encode relations as translation operations in that space, have achieved better performance. However, most of them have limitations in expressing complex relations for knowledge graph. In this paper, we propose a translation-neural based method NTransGH for knowledge graph completion. NTransGH combines translation mechanism for modeling relations as translation operations by generalized hyperplanes, and a neural network for capturing more complex interactions between entities and relations. We conduct experiment on two tasks link prediction and triplet classification with two datasets. Experimental results show that NTransGH has strong expression in mapping properties of complex relations, and achieves significant and consistent improvements over state-of-the-art embedding methods. This paper is an extension of our previous works [1]. (C) 2018 Elsevier B.V. All rights reserved.

关键词： knowledge representation knowledge embedding knowledge graph completion Link prediction Neural network

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