Co-learning binary classifiers for LP-based multi-label classification

Shan, Jincheng; Hou, Chenping; Hong, Tao; Zhuge, Wenzhang; Yi, Dongyun

doi:10.1016/j.cogsys.2019.01.009

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…The extracted features are used to train the multi-label ECG classifier. , BRSVM [4], MLKNN [38], MLHARAM [39], MLSVM [40], Label Powerset [41], Class Chain [42] and LSPC [43], are shown in Table 4. The models marked with an asterisk in Table 4 are methods with added preprocessing and the number of classes is six, as shown in Table 1.…”

Section: Results Analysismentioning

confidence: 99%

“…A total of 60% of the samples are randomly selected for training and the remaining 40% are used for testing, and five-fold cross-validation is used to validate the results. The average classification results based on each multi-label classifier, i.e., BRSVM [ 4 ], MLKNN [ 38 ], MLHARAM [ 39 ], MLSVM [ 40 ], Label Powerset [ 41 ], Class Chain [ 42 ] and LSPC [ 43 ], are shown in Table 4 . The models marked with an asterisk in Table 4 are methods with added preprocessing and the number of classes is six, as shown in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

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Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Yang

Lan

et al. 2022

Bioengineering

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There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments.

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Section: Results Analysismentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Yang

Lan

et al. 2022

Bioengineering

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“…In the multi-label classification problem, after transforming the multi-label problem with tags into a multi-category classification problem, there exist possible label pairs, and these label pairs are mapped to natural numbers as the class labels in the multi-category classification. Tsoumakas and Vlahavas [22] proposed an ensemble method for multi-label classification. The algorithm uses the LP classifier to train a subset of length in the label set, and then integrates a large number of label powerset (LP) classifiers to make predictions.…”

Section: C Cmentioning

confidence: 99%

Multi-Label Image Classification with Weak Correlation Prior

Ouyang

Fan

Hong

et al. 2022

CAAI Artificial Intelligence Research

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Image classification is vital and basic in many data analysis domains. Since real-world images generally contain multiple diverse semantic labels, it amounts to a typical multi-label classification problem. Traditional multi-label image classification relies on a large amount of training data with plenty of labels, which requires a lot of human and financial costs. By contrast, one can easily obtain a correlation matrix of concerned categories in current scene based on the historical image data in other application scenarios. How to perform image classification with only label correlation priors, without specific and costly annotated labels, is an important but rarely studied problem. In this paper, we propose a model to classify images with this kind of weak correlation prior. We use label correlation to recapitulate the sample similarity, employ the prior information to decompose the projection matrix when regressing the label indication matrix, and introduce the norm to select features for each image. Finally, experimental results on several image datasets demonstrate that the proposed model has distinct advantages over current state-of-the-art multilabel classification methods.

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“…It transforms multi-label learning problem into binary classification problems which encode the label correlations into feature space and constructs the models according to a chaining order specified over the class labels. Label powerset (LP) transforms problems to multiclass classification which regards the subsets of the label space as new labels [35]. It makes use of ensemble learning to improve the LP algorithm by randomly selecting k labels each time and using the ensemble learning technique to get the final results.…”

Section: Related Workmentioning

confidence: 99%

“…The method maps each combination to a unique combination id number, and performs multi-class classification using the classifier as a multi-class classifier and combination ids as classes. Detail information is referenced in [35].…”

Section: ) Label Powersetmentioning

confidence: 99%

Multi-Label ECG Signal Classification Based on Ensemble Classifier

et al. 2020

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Electrocardiogram (ECG) has been proved to be the most common and effective approach to investigate the cardiovascular disease because that it is simple, non-invasive and low cost. ECG signal automatic classification is a popular research topic and some efficient research work has been done on it. Most of current research work focuses on single ECG label classification, i.e. one ECG signal record corresponds to one label. In practice, one ECG signal usually embraces several cardiovascular diseases at the same time. It is more important to study multi-label ECG signal classification. To our knowledge, few research works have been done on the research topic. To resolve the multi-label ECG signal classification problems, we propose a novel ensemble multi-label classification model in this paper. The model combines several multi-label classification methods to generate a high performance classifier. Mutual information is used to measure the weight of each classifier. At last the ensemble multi-label classification model is used to analyze a clinic ECG signal dataset. The analysis results show that the overall classification performance is improved. It provides a feasible analysis method for multi-label ECG signal automatic classification.

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Co-learning binary classifiers for LP-based multi-label classification

Cited by 11 publications

References 14 publications

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Multi-Label Image Classification with Weak Correlation Prior

Multi-Label ECG Signal Classification Based on Ensemble Classifier

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