Joint Feature Selection and Classification for Multilabel Learning

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

doi:10.1109/tcyb.2017.2663838

Cited by 132 publications

(66 citation statements)

References 46 publications

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“…So far, many methods have been developed to improve the performance of multi-label learning by exploring various types of label correlations (Tsoumakas et al 2009;Cesa-Bianchi, Gentile, and Zaniboni 2006;Petterson and Caetano 2011;Huang, Zhou, and Zhou 2012;Huang, Yu, and Zhou 2012;Zhu, Kwok, and Zhou 2018). There has been increasing interest in exploiting the label correlations by taking the label correlation matrix as prior knowledge (Hariharan et al 2010;Cai et al 2013;Huang et al 2016;Huang et al 2018). Concretely, these methods directly calculate the label correlation matrix by the similarity between label vectors using common similarity measures, and then incorporate the label correlation matrix into model training for further enhancing the predictions of multiple label assignments.…”

Section: Introductionmentioning

confidence: 99%

Collaboration Based Multi-Label Learning

Feng

He³

2019

AAAI

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It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels. Besides, label correlations are normally used to regularize the hypothesis space, while the final predictions are not explicitly correlated. In this paper, we suggest that for each individual label, the final prediction involves the collaboration between its own prediction and the predictions of other labels. Based on this assumption, we first propose a novel method to learn the label correlations via sparse reconstruction in the label space. Then, by seamlessly integrating the learned label correlations into model training, we propose a novel multi-label learning approach that aims to explicitly account for the correlated predictions of labels while training the desired model simultaneously. Extensive experimental results show that our approach outperforms the state-of-the-art counterparts.

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Section: Introductionmentioning

confidence: 99%

Collaboration Based Multi-Label Learning

Feng

He³

2019

AAAI

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“…Different from binary classification scenarios, where each sample is associated with only one single semantic label, multi-label learning aims at assigning a set of discrete nonexclusive labels to a sample, and has received increasing interest in different machine learning tasks [20]. For instance, [21] and [22] assume that fully supervised signals are available and focus on learning multi-label classifiers under supervised setting. Such assumption, however, may not hold in realworld applications, because it requires exhaustive efforts to annotate multi-label samples.…”

Section: A Multi-label Learningmentioning

confidence: 99%

Individuality- and Commonality-Based Multiview Multilabel Learning

Tan

Wang

et al. 2021

IEEE Trans. Cybern.

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In multi-view multi-label learning, each object is represented by several heterogeneous feature representations and is also annotated with a set of discrete non-exclusive labels. Previous studies typically focus on capturing the shared latent patterns among multiple views, while do not sufficiently consider the diverse characteristics of individual views, which can cause performance degradation. In this paper, we propose a novel approach (ICM2L) to explicitly explore the individuality and commonality information of multi-label multiple view data in a unified model. Specifically, a common subspace is learned across different views to capture the shared patterns. Then, multiple individual classifiers are exploited to explore the characteristics of individual views. Next, an ensemble strategy is adopted to make prediction. Finally, we develop an alternative solution to jointly optimize our model, which can enhance the robustness of the proposed model towards rare labels and reinforce the reciprocal effects of individuality and commonality among heterogeneous views, and thus further improve the performance. Experiments on various real-word datasets validate the effectiveness of ICM2L against state-of-the-art solutions, and ICM2L can leverage the individuality and commonality information to achieve an improved performance as well as to enhance the robustness toward rare labels.

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“…Multi-label feature selection approach using Relief and Information Gain (IG) is discussed in [13]. A novel approach which jointly performs feature selection with classification called the joint feature selection and classification for multi-label learning (JFSC) is proposed by [14]. Distribution based feature selection measure Chi square is used with label power set as a problem transformation technique [15].…”

Section: Related Workmentioning

confidence: 99%

Accuracy Based Feature Ranking Metric for Multi-Label Text Classification

Asim¹,

Rehman²,

Shoaib³

2017

ijacsa

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Abstract-In many application domains, such as machine learning, scene and video classification, data mining, medical diagnosis and machine vision, instances belong to more than one categories. Feature selection in single label text classification is used to reduce the dimensionality of datasets by filtering out irrelevant and redundant features. The process of dimensionality reduction in multi-label classification is a different scenario because here features may belong to more then one classes. Label and instance space is rapidly increasing by the grandiose of Internet, which is challenging for Multi-Label Classification (MLC). Feature selection is crucial for reduction of data in MLC. Method adaptation and data set transformation are two techniques used to select features in multi label text classification. In this paper, we present dataset transformation technique to reduce the dimensionality of multi-label text data. We used two model transformation approaches: Binary Relevance, and Label Power set for transformation of data from multi-label to single label. The Process of feature selection is done using filter approach which utilizes the data to decide the importance of features without applying learning algorithm. In this paper we used a simple measure (ACC2) for feature selection in multi-label text data. We used problem transformation approach to apply single label feature selection measures on multi-label text data; did the comparison of ACC2 with two other feature selection methods, information gain (IG) and Relief measure. Experimentation is done on three bench mark datasets and their empirical evaluation results are shown. ACC2 is found to perform better than IG and Relief in 80% cases of our experiments.

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Joint Feature Selection and Classification for Multilabel Learning

Cited by 132 publications

References 46 publications

Collaboration Based Multi-Label Learning

Collaboration Based Multi-Label Learning

Individuality- and Commonality-Based Multiview Multilabel Learning

Accuracy Based Feature Ranking Metric for Multi-Label Text Classification

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