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An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2018年第3期9卷 268-277页

作者： Liu, Chanjuan Chen, Tongtong Zou, Hailin Ding, Xinmiao Wang, Yuling Ludong Univ Sch Informat & Elect Engn Prov Key Lab Cyber Phys Syst & Intelligent Contro Yantai 264025 Peoples R China Shandong Inst Business & Technol Sch Informat & Elect Engn Yantai 264005 Peoples R China Shandong Jianzhu Univ Dept Informat & Elect Engn Jinan 250101 Shandong Peoples R China

multi-instance multi-label learning (MIML) has been successfully used in image and text classification problems. It is noteworthy that few of the previous studies consider the pattern-label relations. Inevitably, there are some useless instances in a bag which will reduce the accuracy of the annotation. In this paper we focus on this problem. Firstly, an instance selection method via joint l(2,1)-norms constraint is employed to eliminate the useless instances and select the representative instances by modelling the instance correlation. Then, bags are mapped to these representative instances. Finally, the classifier is trained by an optimisation algorithm based on label correlations. Experimental results on image data set, text data sets and bird song audio data set show that the proposed algorithm significantly improves the performance of MIML classifier compared with the state-of-the-art methods.

关键词： multi-instance multi-label learning representative instances selection joint l(2,1)-norms constraint label correlations

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MIMLTWSVM: Twin Support Vector Machine for multi-instance multi-label learning 11

MIMLTWSVM: Twin Support Vector Machine for Multi-Instance Mu...

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11th International Conference on Industrial and Information Systems (ICIIS)

作者： Tomar, Divya Agarwal, Sonali Indian Inst Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781509038183

Recently, multi instance multi label (MIML) learning has attracted the attention of researchers in which an example not only belongs to multiple instances but also associated with multiple class labels. This study proposes a novel multiinstance multi-label twin support vector machine (MIMLTWSVM) classifier by extending the recently proposed binary twin support vector machine (TWSVM) classifier. MIMLTWSVM classifier involves two steps-(1) the problem of multi-instance multi-label has been converted to single instance multi label learning in the first step and (2) in the second step, the derived problem is solved by using multi-label twin support vector machine classifier. The involved Quadratic Programming Problems (QPPs) of proposed classifier has been solved by Successive Over-Relaxation (SOR) technique to speed up the training procedure. The experiment has been conducted on two MIML benchmark datasets-Scene and Reuters. The experimental results demonstrate the superiority of the proposed classifier over several existing state-of-the-art MIML classifiers such as MIMLSVM, MIMLRBF, MIMLBOOST, MIML-kNN and (MMIML)-M-3.

关键词： multi-instance multi-label learning Twin Support Vector Machine Succesive Over-Relaxtion

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Selecting Suitable Image Retargeting Methods with multi-instance multi-label learning

Selecting Suitable Image Retargeting Methods with Multi-inst...

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International Conference on Brain and Health Informatics (BHI)

作者： Song, Muyang Ren, Tongwei Liu, Yan Bei, Jia Zhao, Zhihong Nanjing Univ Software Inst Nanjing Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783319027531;9783319027524

Althogh the diversity of mobile devices brings in image retargeting technique to effectively display images on various screens, no existing image retargeting method can handle all images well. In this paper, we propose a novel approach to select suitable image retargeting methods solely based on original image characteristic, which can obtain acceptable selection accuracy with low computation cost. First, the original image is manually annotated with several simple features. Then, suitable methods are automatically selected from candidate image retargeting methods using multi-instance multi-label learning. Finally, target images are generated by the selected methods. Experiments demonstrate the effectiveness of the proposed approach.

关键词： Image retargeting method selection image characteristic analysis multi-instance multi-label learning

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A multi-instance multi-label learning Algorithm Based on Feature Selection 10

A Multi-instance Multi-label Learning Algorithm Based on Fea...

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10th International Conference on Broadband and Wireless Computing, Communication and Applications

作者： Chen Tong-tong Liu Chan-juan Zou Hai-lin Shen Qian Liu Ying Ding Xin-miao Ludong Univ Sch Informat & Elect Engn Yantai Peoples R China Shandong Inst Business & Technol Sch Informat & Elect Engn Yantai Peoples R China

ISBN: (纸本)9781467383158

multi-instance multi-label learning is an extension of multi-instance learning for multi-label classification. In order to select typical instances with high discrimination for multiple labels, the feature selection via Joint l(2,1) -norms minimization is introduced in this paper, and a multi-instance multi-label learning algorithm based on feature selection is proposed. All bags are mapped to typical instances after feature selection, and then the classifier considering label correlations is trained. Experimental results show that the proposed algorithm greatly improves the performance of multi-instance multi-label classifier compared with other methods.

关键词： multi-instance multi-label learning feature selection typical instances label correlations

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HMIML: Hierarchical multi-instance multi-label learning of Drosophila Embryogenesis Images Using Convolutional Neural Networks

HMIML: Hierarchical Multi-Instance Multi-Label Learning of <...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Li, Tiange Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781538654880

The Drosophila embryonic gene expression images provide important spatio-temporal expression information for understanding the mechanisms of Drosophila embryogenesis. Automatic annotation of these images is an imperative but challenging task. Unlike the auto-annotation for nature images, the labels (terms from a controlled vocabulary) are assigned to genes rather than images. Each gene corresponds to a set of images, and different genes are associated with different numbers of images and labels, thus conventional machine learning methods are not applicable in such a scenario. In this study, we treat this task as a multi-instance multi-label (MIML) problem, and propose a hierarchical MIML learning framework, called HMIML. We implement HMIML at image-level and gene-level, respectively, both using convolutional neural networks. Especially, an image stitching strategy is employed to get a combined image representation at gene-level. Experimental results on the FlyExpress database show that HMIML enhances annotation accuracy on all developmental stages compared with the existing methods.

关键词： gene expression Drosophila embryonic image multi-instance multi-label learning convolutional neural network

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DEEP FEATURE REPRESENTATIONS AND multi-instance multi-label learning OF WHOLE SLIDE BREAST HISTOPATHOLOGY IMAGES

DEEP FEATURE REPRESENTATIONS AND MULTI-INSTANCE MULTI-LABEL ...

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作者： Caner Mercan Bilkent University

学位级别：博士

The examination of a tissue sample has traditionally involved a pathologist investigating the case under a microscope. Whole slide imaging technology has recently been utilized for the digitization of biopsy slides, replicating the microscopic examination procedure with the computer screen. This technology made it possible to scan the slides at very high resolutions, reaching up to 100, 000 × 100, 000 pixels. The advancements in the imaging technology has allowed the development of automated tools that could help reduce the workload of pathologists during the diagnostic process by performing analysis on the whole slide histopathology images. One of the challenges of whole slide image analysis is the ambiguity of the correspondence between the diagnostically relevant regions in a slide and the slide-level diagnostic labels in the pathology forms provided by the pathologists. Another challenge is the lack of feature representation methods for the variable number of variable-sized regions of interest (ROIs) in breast histopathology images as the state-of-the-art deep convolutional networks can only operate on fixed-sized small patches which may cause structural and contextual information loss. The last and arguably the most important challenge involves the clinical significance of breast histopathology, for the misdiagnosis or the missed diagnoses of a case may lead to unnecessary surgery, radiation or hormonal therapy. We address these challenges with the following contributions. The first contribution introduces the formulation of the whole slide breast histopathology image analysis problem as a multi-instance multi-label learning (MIMLL) task where a slide corresponds to a bag that is associated with the slide-level diagnoses provided by the pathologists, and the ROIs inside the slide correspond to the instances in the bag. The second contribution involves a novel feature representation method for the variable number of variable-sized ROIs using the activations of d

关键词： multi-instance multi-label learning deep convolutional features whole slide imaging breast histopathology digital pathology medical image analysis

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Deep multi-instance multi-label learning for Image Annotation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第3期32卷 1859005-1859005页

作者： Guo, Hai-Feng Han, Lixin Su, Shoubao Sun, Zhou-Bao Hohai Univ Coll Comp & Informat Nanjing 210024 Jiangsu Peoples R China Jinling Inst Technol Sch Comp Engn Nanjing 211169 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Nanjing Audit Univ Nanjing 211815 Jiangsu Peoples R China

multi-instance multi-label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to identify its equivalence in the traditional supervised learning framework. Motivated by the recent advancement in deep learning, in this paper, we still consider the problem of predicting labels and attempt to model deep learning in MIML learning framework. The proposed approach enables us to train deep convolutional neural network with images from social networks where images are well labeled, even labeled with several labels or uncorrelated labels. Experiments on real-world datasets demonstrate the effectiveness of our proposed approach.

关键词： multi-instance multi-label learning deep learning convolutional neural network

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Hierarchical multi-instance multi-label learning for Chinese patent text classification

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CONNECTION SCIENCE 2024年第1期36卷

作者： Liu, Yunduo Xu, Fang Zhao, Yushan Ma, Zichen Wang, Tengke Zhang, Shunxiang Tian, Yuhao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Anhui Univ Sci & Technol Sch Foreign Languages Huainan Peoples R China Anhui Univ Sci & Technol Sch Math & Big Data Huainan Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

To further enhance the accuracy of the Chinese patent classification, this paper proposes a model, based on the patent structure and takes the patent claim as subjects, with multi-instance multi-label learning as the main method. Firstly, the patent claims are divided into multiple independent texts using the sequence number as the splitting token. For each patent, multiple claims are regarded as multiple instances, and the corresponding IPCs serve as its multiple labels. Next, the concept of secondary_label is introduced following the composition rules of IPC, and the relationships between instances and multiple secondary_labels are mined through the construction of fully-connected layers. To capture more comprehensive semantic information of instances, BIGRU and self-attention are employed to enhance semantics and reduce information loss during the training process. Finally, the max-pooling operations are utilised to obtain the predicted categories of patents based on capturing the relationships between instances and different hierarchical labels. Experimental results on the '2017 Chinese patent dataset' demonstrate that the multi-instance multi-label approach can effectively mine deeper relationships between patents and labels in classification tasks. As a result, our model significantly improves the accuracy of patent text classification.

关键词： Patent text classification IPC secondary_label multi-instance multi-label learning patent claim

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A FRAMEWORK OF HASHING FOR multi-instance multi-label learning

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第3期11卷 921-934页

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

multi-instance multi-label learning (MIML) is a powerful framework, which deals with the problem that each example is represented as multiple instances and associated with multiple class labels. Previous works mostly focus on accuracy, while scalability for large scale datasets has been rarely addressed. In this paper, we present a novel framework - multi-instance multi-label Hashing (MIMLH) to tackle both accuracy and scalability issues of MIML tasks, which means that it can not only get good accuracy, but also fast learning speed. MIMLH leverages hashing technique. Specifically, it exploits the hashing approach in two perspectives - bag-level hashing and instance-level hashing, which replaces the dot-product kernel operator in the previous methods and effectively maps the entire samples into hamming space, speeding up the process of learning tremendously. Moreover, we also take the label information into account to enhance our framework. We evaluate our approach on two popular data sets of MIML task, which were derived from two real world applications - scene classification and text categorization. The experimental results show that the proposed framework performs better than previous works on accuracy and efficiency in a balanced way.

关键词： multi-instance multi-label learning Hashing Scene classification Text categorization

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Predicting the function of rice proteins through multi-instance multi-label learning based on multiple features fusion

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac095-bbac095页

作者： Liu, Jing Tang, Xinghua Cui, Shuanglong Guan, Xiao Shanghai Maritime Univ Informat Engn Coll Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China

There are a large number of unannotated proteins with unknown functions in rice, which are difficult to be verified by biological experiments. Therefore, computational method is one of the mainstream methods for rice proteins function prediction. Two representative rice proteins, indica protein and japonica protein, are selected as the experimental dataset. In this paper, two feature extraction methods (the residue couple model method and the pseudo amino acid composition method) and the Principal Component Analysis method are combined to design protein descriptive features. Moreover, based on the state-of-the-art MIML algorithm EnMIMLNN, a novel MIML learning framework MK-EnMIMLNN is proposed. And the MK-EnMIMLNN algorithm is designed by learning multiple kernel fusion function neural network. The experimental results show that the hybrid feature extraction method is better than the single feature extraction method. More importantly, the MK-EnMIMLNN algorithm is superior to most classic MIML learning algorithms, which proves the effectiveness of the MK-EnMIMLNN algorithm in rice proteins function prediction.

关键词： rice protein function prediction machine learning multi-instance multi-label learning feature extraction kernel function

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