Topic
Classifier chains
About: Classifier chains is a research topic. Over the lifetime, 170 publications have been published within this topic receiving 20989 citations.
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TL;DR: This paper studies and develops methods for regressor chains, and presents a sequential Monte Carlo scheme in the framework of a probabilistic regressor chain that can be effective, flexible and useful in several types of data.
8 citations
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22 Aug 2017TL;DR: Posters usually highlight a movie scene or characters, and at the same time should inform about the genre or the plot of the movie to attract the potential audience, so the assumption was that the relevant information can be captured in visual features.
Abstract: Classification of movies into genres from the accompanying promotional materials such as posters is a typical multi-label classification problem. Posters usually highlight a movie scene or characters, and at the same time should inform about the genre or the plot of the movie to attract the potential audience, so our assumption was that the relevant information can be captured in visual features.
8 citations
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24 Jul 2016TL;DR: The proposed method is a competitive approach for solving multi-label classification tasks with large number of labels and a variant of classifier chain strategy is implemented to enhance the multi- label learning system.
Abstract: In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.
8 citations
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TL;DR: This work proposes a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm, which can make convince prediction with partial modalities for MMML problem and reveals that it may be better to extract many instead of all of the modalities at hand.
Abstract: With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels are inconsistent and some of them may not benefit for prediction. In real life, not all the modalities are needed for prediction. As a result, we propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm for MMML problem, which can make convince prediction with partial modalities. MCC extracts different modalities for different instances in the testing phase. Extensive experiments are performed on one real-world herbs dataset and two public datasets to validate our proposed algorithm, which reveals that it may be better to extract many instead of all of the modalities at hand.
8 citations
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17 Dec 2019TL;DR: This paper proposes a neural network algorithm, CascadeML, to train multi-label neural network based on cascade neural networks, which requires minimal or no hyperparameter tuning and also considers pairwise label associations.
Abstract: In multi-label classification a datapoint can be labelled with more than one class at the same time. A common but trivial approach to multi-label classification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate hyperparameter tuning, a computationally expensive optimisation problem. Tuning is important to train a good multi-label classifier model. There is a scarcity in the literature of effective multi-label classification approaches that do not require extensive hyperparameter tuning. This paper addresses this scarcity by proposing CascadeML, a multi-label classification approach based on cascade neural network that takes label associations into account and requires minimal hyperparameter tuning. The performance of the CasecadeML approach is evaluated using 10 multi-label datasets and compared with other leading multi-label classification algorithms. Results show that CascadeML performs comparatively with the leading approaches but without a need for hyperparameter tuning.
8 citations