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Predicting multiple Functions of Sustainable Flood Retention Basins under Uncertainty via multi-instance multi-label learning

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WATER 2015年第4期7卷 1359-1377页

作者： Yang, Qinli Boehm, Christian Scholz, Miklas Plant, Claudia Shao, Junming Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu 611731 Peoples R China Univ Munich Inst Comp Sci D-80937 Munich Germany Univ Salford Sch Comp Sci & Engn Civil Engn Res Grp Salford M5 4WT Lancs England Helmholtz Zentrum Munich German Res Ctr Environm Hlth D-85764 Neuherberg Germany

The ambiguity of diverse functions of sustainable flood retention basins (SFRBs) may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML) learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty), and the MIML-support vector machine (SVM) algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty). Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%). The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

关键词： sustainable flood retention basin function assessment uncertainty multi-instance multi-label learning classification

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MIMLRBF: RBF neural networks for multi-instance multi-label learning

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NEUROCOMPUTING 2009年第16-18期72卷 3951-3956页

作者： Zhang, Min-Ling Wang, Zhi-Jian Hohai Univ Coll Comp & Informat Engn Nanjing 210098 Peoples R China

In multi-instance multi-label learning (MIML), each example is not only represented by multiple instances but also associated with multiple class labels. Several learning frameworks, such as the traditional supervised learning. can be regarded as degenerated versions of MIML. Therefore, an intuitive way to solve MIML problem is to identify its equivalence in its degenerated versions. However, this identification process would make useful information encoded in training examples get lost and thus impair the learning algorithm's performance. In this paper, RBF neural networks are adapted to learn from MIML examples. Connections between instances and labels are directly exploited in the process of first layer clustering and second layer optimization. The proposed method demonstrates superior performance on two real-world MIML tasks. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Machine learning multi-instance multi-label learning Radial basis function Scene classification Text categorization

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Genome-Wide Protein Function Prediction through multi-instance multi-label learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014年第5期11卷 891-902页

作者： Wu, Jian-Sheng Huang, Sheng-Jun Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210046 Jiangsu Peoples R China

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multi-domain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently multi-instance multi-label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and multi-label learning algorithms.

关键词： Protein function prediction genome wide machine learning multi-instance multi-label learning Hausdorff distances ensemble learning

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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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Dynamic Programming for instance Annotation in multi-instance multi-label learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年第12期39卷 2381-2394页

作者： Pham, Anh T. Raich, Raviv Fern, Xiaoli Z. Oregon State Univ Sch EECS Corvallis OR 97331 USA

labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our method using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.

关键词： multi-instance multi-label learning instance annotation expectation maximization graphical model dynamic programming

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Automatic Waveform Recognition of Overlapping LPI Radar Signals Based on multi-instance multi-label learning

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 1275-1279页

作者： Pan, Zesi Wang, Shafei Zhu, Mengtao Li, Yunjie Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Northern Inst Elect Equipment Beijing 100091 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel multi-instance multi-label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.

关键词： Radar Modulation Signal to noise ratio Feature extraction Robustness Task analysis Training LPI radar signals overlapping signal recognition multi-instance multi-label learning deep convolutional neural network

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Transductive multi-instance multi-label learning algorithm with application to automatic image annotation

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 661-670页

作者： Feng, Songhe Xu, De Beijing Jiaotong Univ Inst Comp & Informat Technol Beijing Peoples R China

Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the scarcity of training examples, the annotation task has become a challenge to systematically develop robust annotation models with better performance. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is usually described by multiple semantic labels (keywords) and these labels are often highly related to respective regions rather than the entire image. In this paper, we propose an improved Transductive multi-instance multi-label (TMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. The experiments over the well known Corel 5000 data set demonstrate that the proposed method is beneficial in the image annotation task and Outperforms most existing image annotation algorithms. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Automatic image annotation Transductive learning Manifold-ranking multi-instance multi-label learning Reinforced diverse density algorithm Gaussian mixture model

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Fast multi-instance multi-label learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年第11期41卷 2614-2627页

作者： Huang, Sheng-Jun Gao, Wei Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China

In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and simultaneously be associated with multiple labels. Such tasks can be formulated as multi-instance multi-label learning (MIML) problems, and have been extensively studied during the past few years. Existing MIML approaches have been found useful in many applications;however, most of them can only handle moderate-sized data. To efficiently handle large data sets, in this paper we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output label semantics.

关键词： multi-instance multi-label learning fast key instance sub-concepts

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Person re-identification based on multi-instance multi-label learning

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NEUROCOMPUTING 2016年 217卷 19-26页

作者： Lin, Ying Guo, Feng Cao, Liujuan Wang, Jinlin Xiamen Univ Sch Informat Sci & Engn Dept Cognit Sci Xiamen Peoples R China Xiamen Univ Sch Informat Sci & Engn Dept Comp Sci Xiamen Peoples R China

The person re-identification is an active research branch of the computer vision and attracts many researchers study on it. However, because of the variance in viewpoints, illumination, pedestrians' pose and some of other factors, building a robust person re-identification algorithm in open-world is rather challenging. In this paper, we propose two algorithms, indiscriminative patches trim strategy and multi instance multi-label learning method, to fix the person re-identification in closed short-term surveillance network. There are two main contributions of this paper: first, we define the discriminative region in a person's image. Correspondingly, an adapted canopy-kmeans algorithm is proposed to evaluate the discriminability of image patches which form the discriminative regions. Besides, two strategies, in local image and in global gallery dataset, are proposed to filter out distractions. Finally, the true discriminative patches are employed to compute the similarity between two images, and then used to re-identify pedestrians. Second, we first introduce the multi-instance multi-label learning methods into re-identification, and hence we propose a framework of solving person re-identification by applying MIMLL. We employ two MIMLL methods, the MIMLBOOST and the MIMLSVM, to detect attributes in each image, and those detected attributes contribute to recognize different pedestrians. Experiments on two benchmark datasets show the competitive performance of the two proposed algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Person re-identification multi-instance multi-label learning Canopy-kmeans

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instance-BASED K-NEAREST NEIGHBOR ALGORITHM FOR multi-instance multi-label learning

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年第5期10卷 1861-1871页

作者： Peng, Peng Xu, Xinshun Wang, Xiaolin Shandong Univ Sch Comp Sci & Technol 1500 Shunhua Rd Jinan 250101 Shandong Peoples R China

multi-instance multi-label learning (MIML) is proposed to tackle the problem represented by a bag of instances and associated with multiple labels which appears in a wide range of real-world tasks. Transforming the MIML problem into an equivalent problem is a very popular way to solve such learning work. However, such transformation may lose useful information encoded in training examples. In this paper we propose a new lazy learning algorithm instance-Based K-Nearest Neighbor (IB-KNN) which transforms MIML to multi-label learning (MLL), but makes full use of information between instances. Specifically, unlike most existing KNN methods for MIML problem which use the distance between bags, IB-KNN uses the distance between instances to discover the neighbor instances. Then, the neighbor instances vote to generate the preliminary results. After that, the problem becomes a standard MLL problem, and KNN method is used again to make the final prediction. We test the proposed algorithm on the COREL image data set and compare it with classical KNN based methods. The results show that IB-KNN achieves better performance.

关键词： multi-instance multi-label learning Lazy learning K-Nearest Neighbor Image annotation

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