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Multi-dimensional classification via stacked dependency exploitation

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Science China(Information Sciences) 2020年第12期63卷 104-117页

作者： Bin-Bin JIA min-ling zhang School of Computer Science and Engineering Southeast University College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of EducationChina Collaborative Innovation Center of Wireless Communications Technology

Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph(DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance *** class space is determined by adaptively stacking predictive outputs from first-level pairwise *** results show that stacked dependency exploitation leads to superior performance against stateof-the-art MDC approaches.

关键词： machine learning multi-dimensional classification class dependencies deterministic strategy stacked dependency exploitation

Compositional metric learning for multi-label classification

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Frontiers of Computer Science 2021年第5期15卷 1-12页

作者： Yan-Ping SUN min-ling zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationChina Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100China

Multi-label classification aims to assign a set of proper labels for each instance,where distance metric learning can help improve the generalization ability of instance-based multi-label classification *** multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature *** this paper,a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label *** one hand,compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on com-ponent *** the other hand,compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label *** to the compositional nature of employed distance metric,the resulting problem admits quadratic programming formulation with linear optimization complexity *** number of training *** also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional *** experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.

关键词： machine learning multi-label learning metric learning compositional metric positive semidefinite matrix decomposition

Multi-dimensional Classification via Selective Feature Augmentation

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Machine Intelligence Research 2022年第1期19卷 38-51页

作者： Bin-Bin Jia min-ling zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented *** this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented ***, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

关键词： Machine learning multi-dimensional classification feature augmentation feature selection class dependencies

Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1248-1261页

作者： Yi-Bo Wang Jun-Yi Hang min-ling zhang School of Computer Science and Engineering Southeast UniversityNanjing 210096China IEEE

Multi-label learning deals with objects associated with multiple class labels,and aims to induce a predictive model which can assign a set of relevant class labels for an unseen *** each class might possess its own characteristics,the strategy of extracting label-specific features has been widely employed to improve the discrimination process in multi-label learning,where the predictive model is induced based on tailored features specific to each class label instead of the identical instance *** a representative approach,LIFT generates label-specific features by conducting clustering ***,its performance may be degraded due to the inherent instability of the single clustering *** improve this,a novel multi-label learning approach named SENCE(stable label-Specific features gENeration for multi-label learning via mixture-based Clustering Ensemble)is proposed,which stabilizes the generation process of label-specific features via clustering ensemble ***,more stable clustering results are obtained by firstly augmenting the original instance repre-sentation with cluster assignments from base clusters and then fitting a mixture model via the expectation-maximization(EM)*** experiments on eighteen benchmark data sets show that SENCE performs better than LIFT and other well-established multi-label learning algorithms.

关键词： Clustering ensemble expectation-maximization al-gorithm label-specific features multi-label learning

Copper modified platinum electrode for amperometric detection of spectinomycin sulfate by anion-exchange chromatography

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Chinese Chemical Letters 2009年第10期20卷 1245-1247页

作者： ling ling Xi Pei min zhang Yan Zhu Department of Chemistry Xixi Campus Zhejiang University Hangzhou 310028 China

A La^3＋-Cu/Pt modified electrode was fabricated by electrodepositing process in CuS04 solution by adding a small amount of lanthium compound, and it was employed for direct current （DC） amperometric detection of spectinomycin by anion-exchange chromatography. Without derivatization, this method can simultaneously determine the main component and impurities in spectinomycin pharmaceutical raw material. Ease of preparation, being applied in DC detection mode and good catalytic stability confirmed the interests of this modified electrode as amperometric sensor for the determination of spectinomycin.

关键词： Copper/platinum chemically modified electrode High performance anion-exchange chromatography Direct current amperometricdetection Spectinomycin

Transfer synthetic over-sampling for class-imbalance learning with limited minority class data

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Frontiers of Computer Science 2019年第5期13卷 996-1009页

作者： Xu-Ying LIU Sheng-Tao WANG min-ling zhang School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Collaborative Innovation Center for Wireless Communications Technology Nanjing 210096 China

The problem of limited minority class data is encountered in manyclass imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Mos t sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.

关键词： machine learning data mining class imbalance over sampling boosting transfer learning

Oral absorption of hyaluronic acid and phospholipids complexes in rats

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World Journal of Gastroenterology 2007年第6期13卷 945-949页

作者： Si-ling Huang Pei-Xue ling Tian-min zhang Working Station for Postdoctoral Scientific Research Institute of Biopharmaceuticals of Shandong Province Jinan 250108 Shandong Province China

AIM： To prepare a complex of hyaluronic acid （HA） and phospholipids （PL）, and study the improvement effect of PL on the oral absorption of HA. METHODS： The complex of HA-PL （named Haplex） was prepared by film dispersion and sonication method, its physico-chemical properties were identified by infrared spectra and differential scanning calorimetry （DSC）. The oral absorption of Haplex was studied. Thirty-two healthy rats were divided into 4 groups randomly：（1） a normal saline （NS） control group; （2） an HA group; （3） a mixture group and （4） a Haplex group. After intragastric administration, the concentration of HA in serum was determined. RESULTS： The physico-chemical properties of Haplex were different from HA or PL or their mixture. After Haplex was administered to rats orally, the serum concentration of HA was increased when compared with the mixture or HA control groups from 4 h to 10 h （P 〈 0.05）. The AAUCo-12, of Haplex was also greater than that of the other three groups （P 〈 0.05）. CONCLUSION： The method of film dispersion and sonication can prepare HA and PL complex, and PL can enhance the oral absorption of exogenous HA.

关键词： Hyaluronic acid Phospholipids Complex Oral absorption Infrared spectrum Thermochemistry

Binary relevance for multi-label learning： an overview

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Frontiers of Computer Science 2018年第2期12卷 191-202页

作者： min-ling zhang Yu-Kun LI Xu-Ying LIU Xin GENG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100 China

Multi-label learning deals with problems where each example is represented by a single instance while being associated with multiple class labels simultaneously. Binary relevance is arguably the most intuitive solution for learning from multi-label examples. It works by decomposing the multi-label learning task into a number of independent binary learning tasks （one per class label）. In view of its potential weakness in ignoring correlations between labels, many correlation-enabling extensions to binary relevance have been proposed in the past decade. In this paper, we aim to review the state of the art of binary relevance from three perspectives. First, basic settings for multi-label learning and binary relevance solutions are briefly summarized. Second, representative strategies to provide binary relevance with label correlation exploitation abilities are discussed. Third, some of our recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced. As a conclusion, we provide suggestions on future research directions.

关键词： machine learning multi-label learning binary relevance label correlation class-imbalance relative labeling-importance

Learning label-specific features for decomposition-based multi-class classification

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Frontiers of Computer Science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG min-ling zhang College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features