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multi-instance multi-label learning based on Gaussian process with application to visual mobile robot navigation

INFORMATION SCIENCES 2012年 190卷 162-177页

作者： He, Jianjun Gu, Hong Wang, Zhelong Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China

Classification problems have been frequently encountered in visual mobile robot navigation. The studies reported so far are mainly focused on the single label problem;i.e., each sample (datum) is assigned to a single class. In the case when a sample belongs to multiple classes simultaneously, most existing approaches attempt to avoid handling this situation by labeling the samples subjectively with the base class, which is the most obvious to them, or by considering them as a new class. In this paper, a new multi-instance multi-label learning (MIML) algorithm, called MIMLGP, is proposed by using Gaussian process (GP) for solving the multiple labels problems in visual mobile robot navigation. Compared with the existing multi-label (ML) algorithms, the MIMLGP method represents each sample with multiple instances so that higher accuracy may be achieved. Moreover, correlations between the labels associated to the same sample, which are crucial to multi-label learning but rarely considered before, are analyzed by using a covariance matrix present in MIMLGP. Experiments in the area of place recognition and terrain classification have been conducted to substantiate the proposed algorithm. The experimental results show that the proposed algorithm can achieve better performance than the one produced by the existing algorithms. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Gaussian process multi-instance multi-label learning Place recognition Terrain classification Visual navigation

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multi-instance multi-label learning

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ARTIFICIAL INTELLIGENCE 2012年第1期176卷 2291-2320页

作者： Zhou, Zhi-Hua Zhang, Min-Ling Huang, Sheng-Jun Li, Yu-Feng Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210046 Jiangsu Peoples R China

In this paper, we propose the MIML (multi-instance multi-label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MIMLBOOST and MIMLSVM algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MIMLSVM algorithm which tackles MIMI, problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the INSDIF and SUBCOD algorithms. INSDIF works by transforming single-instances into the MIML representation for learning, while SUBCOD works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Machine learning multi-instance multi-label learning MIML multi-label learning multi-instance learning

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multi-task MIML learning for pre-course student performance prediction

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Frontiers of Computer Science 2020年第5期14卷 113-121页

作者： Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual *** this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course ***,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the *** this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its *** better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)***,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified *** experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning

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ESRE: handling repeated entities in distant supervised relation extraction

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 11325-11337页

作者： Sun, Xin Jiang, Jinghu Shang, Yuming Beijing Inst Technol Sch Comp Sci Beijing Peoples R China

Distant supervised relation extraction has been widely used to find novel relational facts from unstructured text. As far as we know, nearly all existing relation extraction models assume that each sentence contains precisely one entity pair, i.e., two entities. However, in reality, the datasets constructed by distant supervision have lots of sentences which contain repeated entities. In other words, there may be more than two entities in a sentence. This phenomenon breaks the assumption of existing models and makes them inevitably encounter the attention bias problem;that is, the model focuses on the wrong entities during relation extraction. To alleviate this problem, in this paper, we utilize the idea of ensemble learning and propose a novel distant supervised relation extraction model. The proposed model follows the multi-instance multi-label learning mechanism and conducts relation extraction based on the sentence-bag representations. Specifically, it first tries to identify the most critical entity and keywords, and then it uses voting mechanism to determine the sentence-level and bag-level features. Experimental results show that our proposed model outperforms the state-of-the-art baselines in relation extraction on a popular benchmark dataset, and it also indicates that the proposed model can indeed alleviate the problem caused by repeated-entity phenomenon.

关键词： Relation extraction Repeated entities Ensemble learning Voting-convolution multi-instance multi-label learning

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An Explainable multi-instance multi-label Classification Model for Full Slice Brain CT Images 3rd

An Explainable Multi-Instance Multi-Label Classification Mod...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Song, Changwei Fu, Guanghui Li, Jianqiang Pei, Yan Beijing Univ Technol Sch Software Engn Beijing 100124 Peoples R China Univ Aizu Comp Sci Div Aizu Wakamatsu Fukushima 9658580 Japan

Brain CT is the first choice for diagnosing intracranial diseases. However, the doctors who can accurate diagnosis is insufficient with the increasing number of patients. Nowadays, many computer-aided diagnosis algorithms were developed to help doctors diagnose and reduce time. However, most of the research classifies each slice isolated, regard this as an image-level classification problem. It is not comprehensive enough because many conditions can only be diagnosed by considering adjacent slices and the relationships between diseases. In order to better fit the characteristics of this task, we formal it as a multi-instance multi-label (MIML) learning problem at sequence-level. In this paper, we analyze the difficulties in the brain CT images classification domain And we propose an efficient model that can improve performance, reduce the number of parameters and give model explanations. In our model, the convolution neural network (CNN) extracts the feature vector from each image of a set of full slice brain CT. The multi-instance detect module focuses on the key images which could assist doctors quickly locate suspicious images and avoid mistakes. We evaluated our model on two datasets: CQ500 and RSNA. The Fl scores are 0.897 and 0.854 respectively. The proposed model outperforms the previous sequence-level model SDLM with only a quarter of the parameters. Low computation and high performance make the model have clinical applicability. Copyright (C) 2020 The Authors.

关键词： Brain CT Deep learning multi-instance multi-label learning Model interpretability Sequence-level

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Bayesian multi-instance multi-label learning using Gaussian process prior

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MACHINE learning 2012年第1-2期88卷 273-295页

作者： He, Jianjun Gu, Hong Wang, Zhelong Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China

multi-instance multi-label learning (MIML) is a newly proposed framework, in which the multi-label problems are investigated by representing each sample with multiple feature vectors named instances. In this framework, the multi-label learning task becomes to learn a many-to-many relationship, and it also offers a possibility for explaining why a concerned sample has the certain class labels. The connections between instances and labels as well as the correlations among labels are equally crucial information for MIML. However, the existing MIML algorithms can rarely exploit them simultaneously. In this paper, a new MIML algorithm is proposed based on Gaussian process. The basic idea is to suppose a latent function with Gaussian process prior in the instance space for each label and infer the predictive probability of labels by integrating over uncertainties in these functions using the Bayesian approach, so that the connection between instances and every label can be exploited by defining a likelihood function and the correlations among labels can be identified by the covariance matrix of the latent functions. Moreover, since different relationships between instances and labels can be captured by defining different likelihood functions, the algorithm may be used to deal with the problems with various multi-instance assumptions. Experimental results on several benchmark data sets show that the proposed algorithm is valid and can achieve superior performance to the existing ones.

关键词： multi-label learning Gaussian process multi-instance multi-label learning Laplace approximation

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An Explainable multi-instance multi-label Classification Model for Full Slice Brain CT Images

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IFAC-PapersOnLine 2020年第5期53卷 780-785页

作者： Changwei Song Guanghui Fu Jianqiang Li Yan Pei School of Software Engineering Beijing University of Technology Beijing 100124 China Computer Science Division University of Aizu Aizu-wakamatsu 965-8580 Japan

Brain CT is the first choice for diagnosing intracranial diseases. However, the doctors who can accurate diagnosis is insufficient with the increasing number of patients. Nowadays, many computer-aided diagnosis algorithms were developed to help doctors diagnose and reduce time. However, most of the research classifies each slice isolated, regard this as an image-level classification problem. It is not comprehensive enough because many conditions can only be diagnosed by considering adjacent slices and the relationships between diseases. In order to better fit the characteristics of this task, we formal it as a multi-instance multi-label (MIML) learning problem at sequence-level. In this paper, we analyze the difficulties in the brain CT images classification domain. And we propose an efficient model that can improve performance, reduce the number of parameters and give model explanations. In our model, the convolution neural network (CNN) extracts the feature vector from each image of a set of full slice brain CT. The multi-instance detect module focuses on the key images which could assist doctors quickly locate suspicious images and avoid mistakes. We evaluated our model on two datasets: CQ500 and RSNA. The F1 scores are 0.897 and 0.854 respectively. The proposed model outperforms the previous sequence-level model SDLM with only a quarter of the parameters. Low computation and high performance make the model have clinical applicability.

关键词： Brain CT Deep learning multi-instance multi-label learning Model interpretability Sequence-level

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A Randomized Clustering Forest Approach for Efficient Prediction of Protein Functions

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IEEE ACCESS 2019年 7卷 12360-12372页

作者： Tang, Hong Wang, Yuanyuan Tang, Shaomin Chu, Dianhui Li, Chunshan Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510275 Guangdong Peoples R China Shenzhen Univ Coll Educ Shenzhen 518060 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510630 Guangdong Peoples R China Harbin Inst Technol Dept Comp Sci Weihai 264209 Peoples R China

With the advances in genetic sequencing technology, the automated assignment of protein function has become a key challenge in bioinformatics and computational biology. In nature, many kinds of proteins consist of a variety of structural domains, and each domain almost holds its own function independently or implements a new function in cooperation with neighbors. Thus, a multi-domain protein function prediction problem can be converted into multi-instance multi-label (MIML) learning tasks. In this paper, we propose a novel ensemble MIML algorithm called multi-instance multi-label randomized clustering forest (MIMLRC-Forest) for protein function prediction. In MIMLRC-Forest, we develop a set of hierarchical clustering trees and conduct a label transfer mechanism to identify the relevant function labels in learning process. The clustering tree with a hierarchical structure can handle the multi-label problem by exploiting more discriminable label concepts at higher-level nodes and by transferring less discriminable labels into the lower-level nodes. Then, the label dependency can be computed by aggregating tree labels for protein function prediction. Extensive experiments on five real-world protein data sets show the effectiveness of the proposed algorithm compared with several state-of-the-art baselines, including MIMLSVM, MIMLNN, MIML-kNN, EnMIMLNN, and M3MIML.

关键词： Protein function prediction multi-instance multi-label learning randomized clustering tree Hausdorff distance ensemble learning

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multi-instance multi-label distance metric learning for genome-wide protein function prediction

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2016年 63卷 30-40页

作者： Xu, Yonghui Min, Huaqing Song, Hengjie Wu, Qingyao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (Le., archaea, bacteria, and eukaryote;Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Protein function prediction Genome wide Distance metric learning Machine learning multi-instance multi-label learning

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LEAP: learning to Prescribe Effective and Safe Treatment Combinations for multimorbidity 17

LEAP: Learning to Prescribe Effective and Safe Treatment Com...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhang, Yutao Chen, Robert Tang, Jie Stewart, Walter F. Sun, Jimeng Tsinghua Univ Beijing Peoples R China Georgia Inst Technol Atlanta GA 30332 USA Sutter Hlth Sacramento CA USA

ISBN: (纸本)9781450348874

Managing patients with complex multimorbidity has long been recognized as a difficult problem due to complex disease and medication dependencies and the potential risk of adverse drug interactions. Existing work either uses complicated rule-based protocols which are hard to implement and maintain, or simple statistical models that treat each disease independently, which may lead to sub-optimal or even harmful drug combinations. In this work, we propose the LEAP (LEArn to Prescribe) algorithm to decompose the treatment recommendation into a sequential decision making process while automatically determining the appropriate number of medications. A recurrent decoder is used to model label dependencies and content-based attention is used to capture label instance mapping. We further leverage reinforcement learning to fine tune the model parameters to ensure accuracy and completeness. We incorporate external clinical knowledge into the design of the reinforcement reward to effectively prevent generating unfavorable drug combinations. Both quantitative experiments and qualitative case studies are conducted on two real world electronic health record datasets to verify the effectiveness of our solution. On both datasets, LEAP significantly outperforms baselines by up to 10-30% in terms of mean Jaccard coefficient and removes 99.8% adverse drug interactions in the recommended treatment sets.

关键词： Treatment Recommendation multimorbidity multi-instance multi-label learning

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