A Graphical Model Approach for Multi-Label Classification

Cetiner, Meltem; Akgül, Yusuf Sinan

doi:10.1007/978-3-319-09465-6_7

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…The graphbased MLC methods can be put into two categories, one category focusing on the improvement of the existing MLC algorithms by building corresponding graph models for multi-label datasets, and the other category focusing on the solutions to the MLC problem by combining the SLC algorithms with a graph model. Included in the first category are the improved BR algorithms, 15 the improved classification chain (CC) algorithms, 27,28 and the improved MLKNN algorithm. 29 In the following, we describe each of them succinctly.…”

Section: Ptms and Aamsmentioning

confidence: 99%

“…29 In the following, we describe each of them succinctly. In Cetiner and Akgul, 15 the label independence issue of the BR algorithm is addressed by first assuming the outputs of each binary classifier as observed nodes of a graphical model, and then determining the final label assignments using the standard powerful Bayesian inference for the unobservable nodes. The Neighbor Pair Correlation Chain Classifier (NPC) algorithm 27 constructs a graph of labels based on the latent Dirichlet allocation (LDA) model and acquires the label correlations using the random walk with the restart strategy.…”

Section: Ptms and Aamsmentioning

confidence: 99%

“…(2) Due to multiple labels and possible links among them, their correlations become very complex. 15 For instance, on one hand, it is more likely for a piece of news tagged with ''entertainment'' to have another tag ''sport'' than ''war.'' On the other hand, in a classification of natural scenes with the set of picture labels {''beach,'' ''field,'' ''autumn leaf,'' ''sunset,'' ''mountain,'' ''city''}, it is less possible that a scenery picture is labeled by both ''autumn leaf'' and ''beach.''…”

Section: Introductionmentioning

confidence: 99%

“…For example, considering the application of graph representation to the MLC methods to cope with the above-mentioned problems. However, little progress has been made with these methods and algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] especially in the following aspects. (1) Complexity: in order to construct a graph model, a graph-based MLC method must map the instances of an entire training set to graph vertices, with many instances irrelevant to the test instances causing very high requirements for time and space and causing high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Wang

Wan

et al. 2020

International Journal of Distributed Sensor Networks

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The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts. The integration of the random walk approach in the multi-label classification methods attracts many researchers’ sight. One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. In this article, we propose a novel multi-label classification algorithm based on the random walk graph and the K-nearest neighbor algorithm (named MLRWKNN). This method constructs the vertices set of a random walk graph for the K-nearest neighbor training samples of certain test data and the edge set of correlations among labels of the training samples, thus considerably reducing the overhead of time and space. The proposed method improves the similarity measurement by differentiating and integrating the discrete and continuous features, which reflect the relationships between instances more accurately. A label predicted method is devised to reduce the subjectivity of the traditional threshold method. The experimental results with four metrics demonstrate that the proposed method outperforms the seven state-of-the-art multi-label classification algorithms in contrast and makes a significant improvement for multi-label classification.

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Section: Ptms and Aamsmentioning

confidence: 99%

Section: Ptms and Aamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Wang

Wan

et al. 2020

International Journal of Distributed Sensor Networks

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“…MLC is a generalization of SLC, which makes it a more difficult and general problem in the machine learning community. Due to multiple labels and the possible links between them, multi-label correlations become very complex [ 6 ]. On the one hand, for example, it is more likely for a piece of news tagged with “war” to have another tag “army” than “entertainment”.…”

Section: Introductionmentioning

confidence: 99%

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Wang

Wan

et al. 2020

Entropy

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Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

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A novel bayesian network-based ensemble classifier chains for multi-label classification

Wang,

Zhang,

Chen

et al. 2024

Complex Intell. Syst.

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In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.

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A Graphical Model Approach for Multi-Label Classification

Cited by 3 publications

References 10 publications

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

A novel bayesian network-based ensemble classifier chains for multi-label classification

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