Multilabel Prediction with Probability Sets: The Hamming Loss Case

Destercke, Sébastien

doi:10.1007/978-3-319-08855-6_50

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Hamming loss (HL): Hamming-Loss is the fraction of labels that are incorrectly predicted. (Destercke, 2014). Therefore, hamming loss takes into account the prediction of both an incorrect label and a missing label normalized over the total number of classes and the total number of examples.…”

Section: Performance Evaluation Mif1mentioning

confidence: 99%

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

Pal¹,

Selvakumar²,

Sankarasubbu³

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relationship among labels. In this paper, a graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task. The generated classifiers are applied to sentence feature vectors obtained from the text feature extraction network(BiLSTM) to enable end-to-end training. Attention allows the system to assign different weights to neighbor nodes per label, thus allowing it to learn the dependencies among labels implicitly. The results of the proposed model are validated on five real-world MLTC datasets. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.

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Section: Performance Evaluation Mif1mentioning

confidence: 99%

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

Pal¹,

Selvakumar²,

Sankarasubbu³

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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“…is prediction class, 𝑦 ̂𝑗(𝑖) is actual class, and 𝑦 𝑗 (𝑖) ≠ 𝑦 ̂𝑗(𝑖) is false prediction overall prediction class, in this case, is difference between prediction class and actual class [38].…”

Section: Resultsmentioning

confidence: 99%

Feature selection based on chi-square and ant colony optimization for multi-label classification

Widians,

Wardoyo,

Hartati

2024

IJECE

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Text classification is widely used in organizations with large databases and digital documents. In text classification, there are many features, most of which are redundant. High-dimensional features impact multi-label classification performance. Feature selection is a data processing technique that can overcome this problem. Feature selection techniques have two major approaches: filter and wrapper. This paper proposes a hybrid filter-wrapper technique combining two algorithms: Chi-square (CS) and ant colony optimization (ACO). In the first stage, CS is used to reduce the number of irrelevant features. The ACO method is in the second stage. The ACO is applied to select the efficient features and improve classifier performance. The experiment results show that CS-ACO, CS-grey wolf optimizer (GWO), CS, and without feature selection (FS) have a micro F1-score based multinomial naïve Bayes classifier including 80%, 79.75%, 79.64% and 77.78%. The result indicates that the CS-ACO algorithm is suitable for solving multi-label classification problems.

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“…• Optimal value is zero [118] 38 Fitness (T) [119] - The reader is encouraged to review the cited references for full details on specific metrics.…”

Section: Discussionmentioning

confidence: 99%

Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences

Naser

Alavi

2021

Archit. Struct. Constr.

168

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Multilabel Prediction with Probability Sets: The Hamming Loss Case

Cited by 12 publications

References 13 publications

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

Feature selection based on chi-square and ant colony optimization for multi-label classification

Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences

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