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作者：吴建盛汤丽华韩微晏善成地理与生物信息学院南京邮电大学南京210046 地理与生物信息学院南京邮电大学南京210046 生物电子学国家重点实验室生物科学与医学工程学院东南大学南京210096

Background: Understanding biological functions of proteins is a key challenge in the post-genomic *** to its inherent difficulty and expense, experimental annotation of protein functions cannot scale up to accommodate the vast amount of sequence data already ***, computational methods that predict biological functions of a protein have emerged as an urgent problem at the forefront of computational and molecular biology, and machine learning techniques provide an effective approach for the construction of classifiers for this task.

关键词： Protein Function Prediction Yeast Genome Machine learning multi-instance multi-label learning

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Predicting Protein Functions of Bacteria Genomes via multi-instance multi-label Active learning 3

Predicting Protein Functions of Bacteria Genomes via Multi-i...

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3rd IEEE International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Wu, Jiansheng Zhu, Wenyong Jiang, Ye Sun, Guwei Gao, Yusheng Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538683118

Bacteria are workhorses in the fields of molecular biology, genetics, and biochemistry. Understanding the biological functions of proteins is important for bacteria studies in the post-genomic era. There are a large number of proteins with unknown biological functions in newly discovered bacteria species. Active learning can assist biologists in selecting the most valuable proteins as candidates for assays. Previously, we formulated the protein function prediction problem as a multi-instance multi-label (MIML) learning task. Here, we propose a MIML active learning algorithm MIMLAL-R for protein function annotation of bacteria genomes. MIMLAL-R minimizes an approximated surrogate loss by stochastic gradient descent with the most valuable bag-label pairs, which are chosen by a selection criterion that combines label cardinality inconsistency and diversity. We tested MIMLAL-R on four real bacteria, namely Geobacter sulfurreducens, Azotobacter vinelandii, Shewanella loihica PV-4, and Escherichia coli strain K12. The MIMLAL-R approach achieved excellent performances in all cases. The codes and data can be freely downloaded from https://***/njuptml/MIMLAL-R.

关键词： bacteria protein function prediction multi-instance multi-label learning active 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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Improvement of learning Algorithm for the multi-instance multi-label RBF Neural Networks Trained with Imbalanced Samples

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2013年第4期29卷 765-776页

作者： Li, Cunhe Shi, Guoqiang China Univ Petr Coll Comp & Commun Engn Qingdao 266555 Peoples R China

multi-instance multi-label learning (MIML) is a novel learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-instance multi-label REF neural networks (MIMLRBF) can exploit connections between the instances and the labels of an MIML example directly. However, it is quite often that the numbers of samples in different categories are discrete, i.e., the class distribution is imbalanced. When an MIMLRBF is trained with imbalanced samples, it will produce poor performance for setting the consistent fraction parameter a for all classes. This paper presents an improved approach in learning algorithms used for training MIMLRBF with imbalanced samples. In the first cluster stage, the methodology calculates the initial medoids for each category based on the data density. Afterwards, k-medoids is been invoked to optimize the medoids. The network will take advantage of the well-adjusted units. In the second stage, the weights between the first and second layer are optimized by the singular value decomposition method. The improved approaches could be used in applications with imbalanced samples. Comparing results employing diverse learning strategies shows interesting outcomes as have come out of this paper.

关键词： machine learning radial basis function multi-instance multi-label learning class imbalance neural networks

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Partial multi-label learning With Noisy label Identification

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第7期44卷 3676-3687页

作者： Xie, Ming-Kun Huang, Sheng-Jun Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Pattern Anal & Machine Intelligence Collaborat Innovat Ctr Novel Software Technol & I Nanjing 211106 Peoples R China

Partial multi-label learning (PML) deals with problems where each instance is assigned with a candidate label set, which contains multiple relevant labels and some noisy labels. Recent studies usually solve PML problems with the disambiguation strategy, which recovers ground-truth labels from the candidate label set by simply assuming that the noisy labels are generated randomly. In real applications, however, noisy labels are usually caused by some ambiguous contents of the example. Based on this observation, we propose a partial multi-label learning approach to simultaneously recover the ground-truth information and identify the noisy labels. The two objectives are formalized in a unified framework with trace norm and l(1) norm regularizers. Under the supervision of the observed noisecorrupted label matrix, the multi-label classifier and noisy label identifier are jointly optimized by incorporating the label correlation exploitation and feature-induced noise model. Furthermore, by mapping each bag to a feature vector, we extend PML-NI method into multi-instance multi-label learning by identifying noisy labels based on ambiguous instances. A theoretical analysis of generalization bound and extensive experiments on multiple data sets from various real-world tasks demonstrate the effectiveness of the proposed approach.

关键词： multi-lable learning partial multi-label learning candidate label set noisy label identification multi-instance multi-label 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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Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期22卷 1563-1569页

作者： Li, Cunhe Shi, Guoqiang China Univ Petr Coll Comp & Commun Engn Qingdao 266555 Peoples R China

multi-instance multi-label learning (MIML) is an innovative learning framework where each sample is represented by multiple instances and associated with multiple class labels. In several learning situations, the multi-instance multi-label RBF neural networks (MIMLRBF) can exploit connections between the instances and the labels of an MIML example directly, while most of other algorithms cannot learn that directly. However, the singular value decomposition (SVD) method used to compute the weights of the output layer will cause augmented overall error in network performance when training data are noisy or not easily discernible. This paper presents an improved approach to learning algorithms used for training MIMLRBF. The steepest descent (SD) method is used to optimize the weights after they are initialized by the SVD method. Comparing results employing diverse learning strategies shows interesting outcomes as have come out of this paper.

关键词： Machine learning Neural networks Radial basis function multi-instance multi-label learning

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multi-instance multi-label image classification: A neural approach

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NEUROCOMPUTING 2013年 99卷 298-306页

作者： Chen, Zenghai Chi, Zheru Fu, Hong Feng, Dagan Hong Kong Polytech Univ Elect & Informat Engn Dept Ctr Multimedia Signal Proc Kowloon Hong Kong Peoples R China Chu Hai Coll Higher Educ Dept Comp Sci Tsuen Wan Hong Kong Peoples R China Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Sydney Inst Biomed Engn & Technol Sydney NSW 2006 Australia

In this paper, a multi-instance multi-label algorithm based on neural networks is proposed for image classification. The proposed algorithm, termed multi-instance multi-label neural network (MIMLNN), consists of two stages of multiLayer Perceptrons (MLP). For multi-instance multi-label image classification, all the regional features are fed to the first-stage MLP, with one MLP copy processing one image region. After that, the MLP in the second stage incorporates the outputs of the first-stage MLPs to produce the final labels for the input image. The first-stage MLP is expected to model the relationship between regions and labels, while the second-stage MLP aims at capturing the label correlation for classification refinement. Error Back-Propagation (BP) approach is adopted to tune the parameters of MIMLNN. In view of that traditional gradient descent algorithm suffers from long-term dependency problem, a refined BP algorithm named Rprop is extended to effectively train MIMLNN. The experiments are conducted on a synthetic dataset and the Corel dataset. Experimental results demonstrate the superior performance of MIMLNN comparing with state-of-the-art algorithms for multi-instance multi-label image classification. (c) 2012 Elsevier B.V. All rights reserved.

关键词： multi-instance multi-label learning Image classification Neural networks Synthetic data

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A fast Markov chain based algorithm for MIML learning

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NEUROCOMPUTING 2016年 216卷 763-777页

作者： Ng, Michael K. Wu, Qingyao Shen, Chenyang Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

multi-instance multi-label (MIML) learning is one of challenging research problems in machine learning. In the literature, there are several methods for solving MIML problems. However, they may take a long computational time and have a huge storage cost for large MIML data sets. The main aim of this paper is to propose and develop an efficient Markov Chain learning algorithm for MIML problems, especially for data represented by non-negative features. Our idea is to perform labels classification iteratively through two Markov chains constructed by using objects and features respectively. The classification of objects can be obtained by using labels propagation via training data in the iterative method. Moreover, we demonstrate that the proposed method can be formulated by considering normalized linear kernel. Because linear kernel function is explicit and separable, it is not necessary to compute and store a huge affinity matrix among objects/instances compared with the use of other kernel functions. Therefore, both the storage and computational time of the proposed algorithm are very efficient. Experimental results are presented to show that the classification performance of the proposed method using normalized linear kernel function is about the same as those using the other kernel functions, while the required computational time is much less, which together suggest that the linear kernel can be good enough for MIML problem. Also experimental results on some benchmark data sets are reported to illustrate the effectiveness of the proposed method in one-error, ranking loss, coverage and average precision, and show that it is competitive with the other MIML methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multi-instance multi-label learning Markov Chains Iterative Method labels Propagation

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Incomplete label multiple instance multiple label learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第3期44卷 1320-1337页

作者： Tam Nguyen Raich, Raviv Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label learning, multiple-instance learning, and active learning. Here, we focus on multiple-instance multiple-label learning with missing bag labels. Little research has been done for this challenging yet potentially powerful variant of incomplete supervision learning. We introduce a novel discriminative probabilistic model for missing labels in multiple-instance multiple-label learning. To address inference challenges, we introduce an efficient implementation of the EM algorithm for the model. Additionally, we consider an alternative inference approach that relies on maximizing the label-wise marginal likelihood of the proposed model instead of the joint likelihood. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach. In particular, comparison to state-of-the-art methods shows that our approach introduces a significantly smaller decrease in performance when the proportion of missing labels is increased.

关键词： labeling Training Phase locked loops Birds Numerical models Graphical models Standards Incomplete-label learning learning with missing labels multiple-instance multiple-label learning multi-instance multi-label learning maximum likelihood marginal maximum likelihood EM algorithm graphical models probabilistic models

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