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Human-machine interactive streaming anomaly detection by online self-adaptive forest

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Frontiers of Computer Science 2023年第2期17卷 145-156页

作者： Qingyang LI zhiwen yu Huang XU Bin GUO School of Computer Science Northwestern Polytechnical UniversityXi'an 710129China

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

关键词： anomaly detection human-machine interaction human feedback random space tree ensemble method

Quantifying predictability of sequential recommendation via logical constraints

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Frontiers of Computer Science 2023年第5期17卷 133-143页

作者： En XU zhiwen yu Nuo LI Helei CUI Lina YAO Bin GUO School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China School of Computer Science and Engineering University of New South WalesSydney NSW 2052Australia

The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.

关键词： sequential recommendation information theory predictability

CrowdDesigner:information-rich and personalized product description generation

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Frontiers of Computer Science 2022年第6期16卷 183-185页

作者： Qiuyun ZHANG Bin GUO Sicong LIU Jiaqi LIU zhiwen yu School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China

1 Introduction Withthe rapid development of the E-commerce,more consumers turn to shop online.When online shoppers search for products using keywords,the related products appear with their brief descriptions.We conduct a real-world survey and find that most shoppers are dissatisfied with the existing"onefit-all product descriptions"and they have to spend more time to scan detail pages.However,handcrafting the attractive product descriptions is always costly.

关键词： attractive Designer spend

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Frontiers of Computer Science 2020年第6期14卷 55-69页

作者： Qianchen yu zhiwen yu Zhu WANG Xiaofeng WANG Yongzhi WANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China Shanxi Provincial Key Laboratory for Embedded System Xi'an 710072China College of Computer Science and Engineering North Minzu UniversityYinchuan 750021China College of Earth Exploration Science and Technology Jilin UniversityChangchun 130025China

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

关键词： graph convolutional neural network graph classification overlapping community detection nonparametric Bayesian generative model relational infinite latent feature model Indian buffet process uncollapsed Gibbs sampler posterior inference quality estimation

Relative manifold based semi-supervised dimensionality reduction

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Frontiers of Computer Science 2014年第6期8卷 923-932页

作者： Xianfa CAI Guihua WEN Jia WEI zhiwen yu School of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou 510006 China School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055 China

A well-designed graph plays a fundamental role in graph-based semi-supervised learning; however, the topological structure of a constructed neighborhood is unstable in most current approaches, since they are very sensitive to the high dimensional, sparse and noisy data. This generally leads to dramatic performance degradation. To deal with this issue, we developed a relative manifold based semisupervised dimensionality reduction （RMSSDR） approach by utilizing the relative manifold to construct a better neighborhood graph with fewer short-circuit edges. Based on the relative cognitive law and manifold distance, a relative transformation is used to construct the relative space and the relative manifold. A relative transformation can improve the ability to distinguish between data points and reduce the impact of noise such that it may be more intuitive, and the relative manifold can more truly reflect the manifold structure since data sets commonly exist in a nonlinear structure. Specifically, RMSSDR makes full use of pairwise constraints that can define the edge weights of the neighborhood graph by minimizing the local reconstruction error and can preserve the global and local geometric structures of the data set. The experimental results on face data sets demonstrate that RMSSDR is better than the current state of the art comparing methods in both performance of classification and robustness.

关键词： cognitive law relative transformation relative manifold local reconstruction semi-supervised learning

Mobile crowd photographing: another way to watch our world

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Science China(Information Sciences) 2016年第8期59卷 250-274页

作者： Huihui CHEN Bin GUO zhiwen yu Xingshe ZHOU School of Computer Science Northwestern Polytechnical University School of Computer and Information Engineering Luoyang Institute of Science and Technology

People take and share pictures in the mobile network. Through collecting and computing pictures with built-in contexts, Mobile Crowd Photographing(MCP) can give us a new way to see this world. This paper focuses on participatory picture collection, which is one way of MCP. Three characteristic issues of MCP are proposed, and then our recent work to solve these issues will also be demonstrated.

关键词： mobile crowd photographing task-driven data collection task definition task assignment data selection

A survey on ensemble learning

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Frontiers of Computer Science 2020年第2期14卷 241-258页

作者： Xibin DONG zhiwen yu Wenming CAO Yifan SHI Qianli MA School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Department of Computer Science City University of Hong KongHong Kong SAR 999077China

Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble learning firstly extracts a set of features with a variety of transformations.Based on these learned features,multiple learning algorithms are utilized to produce weak predictive results.Finally,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.

关键词： ensemble learning supervised ensemble classification semi-supervised ensemble classification clustering ensemble semi-supervised clustering ensemble

A framework based on sparse representation model for time series prediction in smart city

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Frontiers of Computer Science 2021年第1期15卷 99-111页

作者： Zhiyong yu Xiangping ZHENG Fangwan HUANG Wenzhong GUO Lin SUN zhiwen yu College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Hangzhou Key Laboratory for IoT Technology&Application Zhejiang University City CollegeHangzhou 310015China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

关键词： sparse representation smart city time series prediction dictionary construction

Cyber-physical-social collaborative sensing： from single space to cross-space

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Frontiers of Computer Science 2018年第4期12卷 609-622页

作者： Fei YI zhiwen yu Huihui CHEN He DU Bin GUO School of Computer Science Northwestern Polytechnical University Xi'an 710072 China

The development of wireless sensor network ing, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding so cial dynamics. Collaborative sensing, which utilizes diver sity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in col laborative sensing, which mainly refers to single space col laborative sensing that consists of physical, cyber, and so cial collaborative sensing. Afterward, we extend this concept into crossspace collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of crossspace collaborative sensing. We also review early works in crossspace collaborative sensing, and study the de tail mechanism based on one typical research work. Finally, although crossspace collaborative sensing is a promising re search area, it ＂is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.

关键词： crossspace collaborative sensing human machine collaboration object matching space association