Selecting label-dependent features for multi-label classification

Qiao, Liyan; Zhang, Limei; Sun, Zhonggui; Liu, Xueyan

doi:10.1016/j.neucom.2016.08.122

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…A good label correlation matrix construction is a crucial step for our framework. In MLL, at present, there are not principle ways for estimating reliable label relationships, and thus it still remains an interesting problem to attempt a new mechanism for learning label relationships [17]. Motivated by [23], instead of specifying any correlation metric or label correlation matrix beforehand, we propose to automatically learn the label correlations with probability, which provides a natural way to obtain correlation information.…”

Section: Learning Local Label Correlationsmentioning

confidence: 99%

“…It can be viewed as a feature mapping method, which lacks interpretability, and does not consider correlation information. Subsequently, a sequence of MLL methods aiming to explore label-specific features is derived from or inspired by LIFT [14], [15], [17], [25], [41]. For example, LLSF [14] and JFSC [15] explore label-specific features for multi-label classification by exploiting the second-order label correlations.…”

Section: Related Workmentioning

confidence: 99%

“…These features could be regarded as label-specific features of the corresponding label. Thus, to explore these label-specific features, some investigations have recently emerged [14]- [17]. While these investigations reported label-specific features are more effective on the discrimination of different labels, both of them can play a key role in the discrimination process.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

et al. 2020

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In multi-label learning, instances can be associated with a set of class labels. The existing multilabel feature selection (MLFS) methods generally adopt either of these two strategies, namely, selecting a subset of features that is shared by all labels (common features) or exploring the most discriminative features for each label (label-specific features). However, both of them can play a key role in the discrimination of different labels. For example, common features can distinguish all labels, and label-specific features contribute to discriminating label's differences. They are important for the discriminability of selected features. On the other hand, it is well-known that exploiting label correlations can advance the performance of MLFS, and label correlations are local and only shared by a data subset in most cases. How to effectively learn and exploit local label correlations in the selection process is significant. In this paper, to address these problems, we propose a novel MLFS framework. Specially, common and label-specific features are simultaneously considered by introducing both l 2,1-norm and l 1-norm regularizers, local label correlations are automatically learned with probability and learned correlation information is efficiently exploited to help feature selection by constraining label correlations on the output of labels. A comparative study with seven state-of-the-art methods manifests the efficacy of our framework.

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Section: Learning Local Label Correlationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

et al. 2020

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“…The first and famous one is LIFT [41], [42]. Others are derived from or inspired by LIFT, e.g., utilizing discriminative features for each Label (ML-DFL) [40], fuzzy rough set (FRS-LIFT) [39], meta-label-specific features [32], label-specific features with class-dependent labels in a sparse stacking way (LLSF-DL) [17], selected label-dependent features (SLEF) [26], label-specific features and local pairwise label correlation (LF-LPLC) [38], and performing joint feature selection and classification [18]. In the next section, we will present the LSDM algorithm which handles multi-label data by reconstructing feature space via label specific discriminant mapping features.…”

Section: Notationsmentioning

confidence: 99%

Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning

Guo

Chung

et al. 2019

ACM Trans. Knowl. Discov. Data

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As an important machine learning task, multi-label learning deals with the problem where each sample instance (feature vector) is associated with multiple labels simultaneously. Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification. One potential drawback of this traditional strategy is that each label might have its own specific characteristics and using identical features for all label cannot lead to optimized performance. In this article, we propose an effective algorithm named LSDM, i.e., leveraging label-specific discriminant mapping features for multi-label learning, to overcome the drawback. LSDM sets diverse ratio parameter values to conduct cluster analysis on the positive and negative instances of identical label. It reconstructs label-specific feature space which includes distance information and spatial topology information. Our experimental results show that combining these two parts of information in the new feature representation can better exploit the clustering results in the learning process. Due to the problem of diverse combinations for identical label, we employ simplified linear discriminant analysis to efficiently excavate optimal one for each label and perform classification by querying the corresponding results. Comparison with the state-of-the-art algorithms on a total of 20 benchmark datasets clearly manifests the competitiveness of LSDM. CCS Concepts: • Computing methodologies → Learning latent representations;

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“…Finally, in high‐order strategies, relation among more labels is considered. In addition to label correlation, there are several recently proposed topics in ML learning algorithms, including class‐imbalance (Li & Wang, n.d.; Spyromitros‐Xioufis, ; Zhang, Li, & Liu, ), label‐specific features (Huang, Li, Huang, & Wu, , ; Qiao, Zhang, Sun, & Liu, ; Xu et al, ; Zhang & LIFT, ), and data streams (Read, Bifet, Holmes, & Pfahringer, ; Song & Ye, ). Class‐imbalance problem happens when instances belonging to a certain label outnumber the instances that do not belong to it in the training set.…”

Section: Fundamental Conceptsmentioning

confidence: 99%

Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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Selecting label-dependent features for multi-label classification

Cited by 18 publications

References 9 publications

Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning

Multilabel feature selection: A comprehensive review and guiding experiments

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