An asymmetric stagewise least square loss function for imbalanced classification

Xu, Guibiao; Hu, Bao-Gang; Prı́ncipe, José C.

doi:10.1109/ijcnn.2014.6889606

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…In this paper, the Asymmetric Stage-wise Least Square (ASLS) loss function [27] is used to replace the general least square loss function. Definition and iteration rules of ASLS can be formalized as follows:…”

Section: ) Loss Functionmentioning

confidence: 99%

Addressing Imbalance in Weakly Supervised Multi-Label Learning

Guo

Chen

2019

IEEE Access

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Multi-label learning has been widely used in many fields to solve the problem of assigning multiple related categories to an instance. Nevertheless, the label for each training example is assumed complete in most of the current multi-label learning methods. As a matter of fact, it is often hard to obtain training samples with complete labels, thus weakly supervised multi-label learning is demanded, which has become a hot topic in recent years. Moreover, the missing labels would further aggravate the class imbalance in multi-label learning. In this paper, the asymmetric stage-wise loss function is introduced to make positive class samples farther away from the classification boundary than negative class samples by adjusting ramp as well as the margin parameters. In addition, the general aggregate loss function is replaced with the average top-k aggregate loss so as to protect the non-typical distributed samples from being sacrificed in the aggregation process of the loss function, and therefore improve the identification accuracy of minority labels. The experiments on both standard and large-scale multi-label data sets demonstrate that the proposed algorithm can solve the problem of class imbalance by changing the sample distribution of training set, thus obtaining competitive performance.

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Section: ) Loss Functionmentioning

confidence: 99%

Addressing Imbalance in Weakly Supervised Multi-Label Learning

Guo

Chen

2019

IEEE Access

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“…In [5] the random walk model is combined with multi -label learning to introduce a multi -label classification algorithm MLRW (Multi-Label Random Walk algorithm). In [6] the asymmetric stage-wise loss function is presented to move the positive class samples at some distance away from the classification boundary compared to the negative class samples by adjustment of the ramp in addition to the margin parameters. In [7] LEML algorithm is used, which is the low-rank property of the label matrix to develop a linear prediction model and then it helps in restoring the missing labels by reducing the kernel norm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification with PSO based Synthetic Minority Over-Sampling Technique (Psosmote) for Imbalanced Samples

Priyadharshini*,

Pavithira

2019

IJRTE

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Recently, the learning from unbalanced data has emerged to be a pre-dominant problem in several applications and in that multi label classification is an evolving data mining task, learning from unbalanced multilabel data is being examined. However, the available algorithms-based SMOTE makes use of the same sampling rate for every instance of the minority class. This leads to sub-optimal performance. To deal with this problem, a new Particle Swarm Optimization based SMOTE (PSOSMOTE) algorithm is proposed. The PSOSMOTE algorithm employs diverse sampling rates for multiple minority class instances and gets the fusion of optimal sampling rates and to deal with classification of unbalanced datasets. Then, Bayesian technique is combined with Random forest for multilabel classification (BARF-MLC) is to address the inherent label dependencies among samples such as ML-FOREST classifier, Predictive Clustering Trees (PCT), Hierarchy of Multi Label Classifier (HOMER) by taking the different metrics including precision, recall, F-measure, Accuracy and Error Rate.

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Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

Fraiwan

Alkhodari

et al. 2021

J Ambient Intell Human Comput

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In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z -score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases. Supplementary Information The online version supplementary material available at 10.1007/s12652-021-03184-y.

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An asymmetric stagewise least square loss function for imbalanced classification

Cited by 5 publications

References 33 publications

Addressing Imbalance in Weakly Supervised Multi-Label Learning

Addressing Imbalance in Weakly Supervised Multi-Label Learning

Multi-Label Classification with PSO based Synthetic Minority Over-Sampling Technique (Psosmote) for Imbalanced Samples

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

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