Multi-label learning with millions of labels

Agrawal, Rahul; Gupta, Archit; Prabhu, Yashoteja; Varma, Manik

doi:10.1145/2488388.2488391

Cited by 171 publications

(17 citation statements)

References 31 publications

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“…We use multilabel classification error as the fitness function for the selected feature subset. Because chromosomes must be evaluated in order to obtain their fitness values, fitness function calls (FFCs) are used in line (4).…”

Section: 2mentioning

confidence: 99%

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Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

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Section: 2mentioning

confidence: 99%

“…These applications produce a series of labels for describing complicated concepts, which are compounded when high-level concepts are composed of multiple subconcepts, such as the environmental and operational conditions of structures [1,4,5]. Let ⊂ R denote a set of patterns constructed from a set of features .…”

Section: Introductionmentioning

confidence: 99%

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

2018

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“…Finally, it is worth highlighting that recent research has shifted its focus to problems on large‐scale problems where the number of labels is assumed to be extremely large . The key challenge being the design of scalable algorithms that offer real‐time predictions, have a small memory footprint and even are able to accommodate missing labels (human annotators tag only with categories they know about).…”

Section: Ongoing Researchmentioning

confidence: 99%

Multi‐label learning: a review of the state of the art and ongoing research

Gibaja

Ventura

2014

WIREs Data Min & Knowl

163

122

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Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.

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“…In one example, an extreme multi-label classifier learns to tag a new Wikipedia article using the subset of the most relevant Wikipedia categories [4]. In another example, a classifier is built to recommend advertisers bid on some search keywords, given their ad landing pages [5].…”

Section: Introductionmentioning

confidence: 99%

Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning

Tagami

2019

IEICE Trans. Inf. & Syst.

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As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large. For example, the number of labels appearing in Web page tagging and E-commerce recommendation tasks reaches hundreds of thousands or even millions. In this paper, we propose a graph partitioning tree (GPT), which is a novel approach for extreme multi-label learning. At an internal node of the tree, the GPT learns a linear separator to partition a feature space, considering approximate k-nearest neighbor graph of the label vectors. We also developed a simple sequential optimization procedure for learning the linear binary classifiers. Extensive experiments on large-scale real-world data sets showed that our method achieves better prediction accuracy than state-of-the-art tree-based methods, while maintaining fast prediction. key words: extreme multi-label classification, k-nearest neighbor graph, tree-based classifier

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Multi-label learning with millions of labels

Cited by 171 publications

References 31 publications

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Multi‐label learning: a review of the state of the art and ongoing research

Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning

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