Regression and classification using extreme learning machine based on L1-norm and L2-norm

Luo, Xiong; Chang, Xiaohui; Ban, Xiaojuan

doi:10.1016/j.neucom.2015.03.112

Cited by 113 publications

(40 citation statements)

References 26 publications

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“…In this way, the label distribution is constructed directly from the sample information of the data by the coefficient matrix. To find the corresponding relevant features while avoiding effects of noise in high dimensional data [21], we introduce sparse reconstruction [22,23]. Sparsity reconstruction is the addition of sparse regularization terms 1 -norm or 0 -norm on the linear reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Label Distribution Learning by Regularized Sample Self-Representation

Yang

Zhao

2018

Mathematical Problems in Engineering

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Multilabel learning that focuses on an instance of the corresponding related or unrelated label can solve many ambiguity problems. Label distribution learning (LDL) reflects the importance of the related label to an instance and offers a more general learning framework than multilabel learning. However, the current LDL algorithms ignore the linear relationship between the distribution of labels and the feature. In this paper, we propose a regularized sample self-representation (RSSR) approach for LDL. First, the label distribution problem is formalized by sample self-representation, whereby each label distribution can be represented as a linear combination of its relevant features. Second, the LDL problem is solved by 2 -norm least-squares and 2,1 -norm least-squares methods to reduce the effects of outliers and overfitting. The corresponding algorithms are named RSSR-LDL2 and RSSR-LDL21. Third, the proposed algorithms are compared with four state-of-the-art LDL algorithms using 12 public datasets and five evaluation metrics. The results demonstrate that the proposed algorithms can effectively identify the predictive label distribution and exhibit good performance in terms of distance and similarity evaluations.

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Section: Related Workmentioning

confidence: 99%

Label Distribution Learning by Regularized Sample Self-Representation

Yang

Zhao

2018

Mathematical Problems in Engineering

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“…ELM has been successfully applied for pattern recognition, image classification, fault diagnosis, big data analytics, and machine learning [6][7][8][9]. ELM has been effectively used for distributed applications parallel computation-based problems [10][11][12][13]. But choosing input weights and bias randomly is an issue to be dealt with.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Deepa¹,

Rajalakshmi²,

Nedunchezhian³

2017

Data-Enabled Discov. Appl.

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Respiratory diseases are emerging as the major health problem across India and Delhi tops in lung diseaserelated issues. The main reason for such rise in respiratory problems in Delhi is air pollution. Among air pollutants, particulate matter (PM 2.5 ) is very hazardous and it is instrumental in causing lung-related diseases. In this paper, an extreme learning machine (ELM) based on statistically controlled activation weight initialization is used to learn and measure the correlation between PM 2.5 and lung-related problems in Delhi. ELM was trained and tested with PM and lung functionality-related medical data. PM 2.5 level data of different areas of Delhi are collected during January 2016 to December 2016. Lung function-related medical data are collected from reputed hospitals in Delhi and analyzed. Results of sputum sample test and spirometry tests conducted on both adults and school children was collected. The test results for both adults and school children are analyzed, and correlation coefficients are evaluated using linear analysis and Spearman's analysis. The correlation results were positive and thus proving that the increase in lung-related diseases in Delhi is directly related to rising PM 2.5 level in the city.

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“…Although ELM is fast and achieves good generalization performance, there is still a lot of room for improvement. Several modifications have been recently introduced in the base of ELM algorithm to improve accuracy and generalization, such as optimally pruned Extreme Learning Machine (OP-ELM) [8] and Regularized-ELM [9–12]. …”

Section: Introductionmentioning

confidence: 99%

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

Abuassba

Zhang

Luo

et al. 2017

Computational Intelligence and Neuroscience

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Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

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Regression and classification using extreme learning machine based on L1-norm and L2-norm

Cited by 113 publications

References 26 publications

Label Distribution Learning by Regularized Sample Self-Representation

Label Distribution Learning by Regularized Sample Self-Representation

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

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