An Empirical Study of Multi-label Learning Methods for Video Annotation

Dimou, Anastasios; Tsoumakas, Grigorios; Mezaris, Vasileios; Kompatsiaris, Ioannis; Vlahavas, Ioannis

doi:10.1109/cbmi.2009.37

Cited by 49 publications

(38 citation statements)

References 14 publications

(21 reference statements)

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Order By: Relevance

“…y , , y = = y y (5) where, classifier h is consisted of L binary classifiers This chaining method passes label information among classifiers, allowing DCC to take label correlations into consideration in the label space in the second layer, thus overcoming the label independence problem of BR.…”

Section: Testing Stagementioning

confidence: 99%

“…As the ability to collect and store large sets of data increased in recent years, multi-label learning has recently received significant attention from machine learning community [5]. It motivated an increasing number of new applications and involved a wide variety of domains, including text classification [6], scene and video classification [7], and bioinformatics [8].…”

Section: Introductionmentioning

confidence: 99%

“…It motivated an increasing number of new applications and involved a wide variety of domains, including text classification [6], scene and video classification [7], and bioinformatics [8]. The algorithms for multi-label classification can be categorized into two different groups [5]: problem transformation methods and algorithm adaptation methods.…”

Section: Introductionmentioning

confidence: 99%

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Double Layer Based Multi-label Classifier Chain

Guo¹,

Li²

2015

TOCSJ

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Abstract:In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for each unseen instance. The widely known binary relevance method (BR) for multi-label classification considers each label as an independent binary problem. It is ignored in the literature due to inadequacy of not considering label correlations. In this paper, we present our double layer based classifier chains method (DCC), which overcomes disadvantages of BR and inherits the benefit of classifier chain method (CC). This algorithm decomposes the multi-label classification problem into two classification processes to generate classifier chain. Each classifier in the chain is responsible for learning and predicting the binary association of the label given the attribute space expanded by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. We also extend this approach further in an ensemble framework. An extensive evaluation covers a broad range of multi-label datasets with a variety of evaluation measures specifically designed for multi-label classification. Experiments on benchmark datasets validate the effectiveness of proposed approach comparing with state-of-art methods in terms of average ranking.

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Section: Testing Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Double Layer Based Multi-label Classifier Chain

Guo¹,

Li²

2015

TOCSJ

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“…Our algorithm is implemented in Java. The software myra-3.7 [41] is adopted, while a Java Library for Multilabel Learning [42] is used to run CLUS-HSC and CLUS-SC. The results in Table 3 show the average accuracy achieved by the cross-validation procedure followed by the standard error of all algorithms in the corresponding datasets.…”

Section: Comparisons Of ℎ Antminer Order With Various Classification mentioning

confidence: 99%

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Liang¹,

Guo²,

Sun³

et al. 2017

Mathematical Problems in Engineering

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Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named ℎ AntMiner order , for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

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“…This is an important result because the proposed method was designed to optimize the above-mentioned quality indicator. Additionally, the introduced procedure can outperform the classification [29,30] and multimedia classification including classification of video objects [12], images [4,57] and music [43]. Another important field of application is bioinformatics where multi-label classification is a powerful tool for prediction of: gene functions [44], protein functions [55,56] or drug resistance [24], to name only a few.…”

Section: Introductionmentioning

confidence: 99%