Laplacian twin extreme learning machine for semi-supervised classification

Li, Shuang; Song, Shiji; Wan, Yihe

doi:10.1016/j.neucom.2018.08.028

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…In this case, if there is no (WindowLABEL, Timestamp) Index track consistent with the timestamp of SID, multi-indexing is applied to the smaller and larger two tracks relatively close to the timestamp. For example, if the timestamp of SID6 is 55, multiple indexing is applied to the tracks close to the SID6, which are (5,50) and (6,60). Since it is hard to present the absolute time of time-series data, the time is relatively presented with the use of timestamp.…”

Section: A Time Window Size Setting For Timing Constraintsmentioning

confidence: 99%

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PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

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This study proposes the prefixSpan based pattern mining using time sliding weight from streaming data. To discover sequential patterns, it applies a time sliding weight to create a structure of projected DB Tree. For the time sliding weight, a time window is applied to the sequential data to calculate the label and support of the window. When a projected DB Tree is designed, the time weight calculated for each pattern is inserted in a table. At this time, the tree is updated by deleting the node whose time weight is less than the reference value. For this reason, whenever data is updated, the tree is sorted again. The reordering process removes the pattern of less influence by applying time weights. Therefore, it is possible to construct a projected DB Tree that can extract influential patterns. The performance of the proposed method is evaluated in three aspects. Firstly, the conventional PrefixSpan algorithm is compared with the proposed PrefixSpan algorithm based on time sliding weight in terms of the pattern generation time according to size of data. Secondly, the fitness of the proposed algorithm is evaluated through cross-validation. Thirdly, GSP, SPADE, and prefixSpan algorithms are compared with the proposed algorithm through F-measure. As a result of the evaluation, the proposed algorithm improves accuracy 75% more than PrefixSpan algorithm and the sequential pattern algorithms of GSP and SPADE. Regarding the comparison of F-measure based on precision and recall, the proposed one improves its performance about 83%.

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Section: A Time Window Size Setting For Timing Constraintsmentioning

confidence: 99%

“…Unsupervised learning is the technique of allowing a computer to learn data without a label in order to find a latent rule [5]. Semi-supervised learning is in a combination of supervised learning and unsupervised learning [6]. Also, reinforcement learning is the technique of determining the order of actions according to rewards [7].…”

Section: Introductionmentioning

confidence: 99%

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

2020

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“…The literature [8,9] notes that ELM has better classification performance than support vector machine (SVM) [10]. Due to the good generalization ability of ELM, ELM has been widely used in pattern recognition [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Globality‐Locality Preserving Maximum Variance Extreme Learning Machine

et al. 2019

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An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.

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“…Aiming at the parameter optimization of ELM and the optimization of single hidden layer activation function, many scholars have conducted research. Li et al [35] proposed using the kernel function in SVM instead of the connection weight matrix between the original hidden layer and the output layer in the ELM algorithm; li et al [36] proposed a new type of Laplacian bipolar to learning machine (LapTELM), enabling LapTELM to fully exploit the benefits of large numbers of unlabeled samples while preserving the learning power and efficiency of the double extreme learning machine (TELM); Fang et al [37] introduced a ELM's multimodal data hierarchical framework which demonstrated that ELM has better learning efficiency than gradient-based multimodal deep learning methods; shang et al [38] developed a classification and regression tree (CART) based on A new predictive model of the Extreme Learning Machine (EELM) method, which improved the accuracy of PM2.5 concentration prediction per hour. Ming et al [39] proposed two parallel changes of ELM including local data and In order to accurately predict the amount and proportion of China's renewable energy terminal power consumption, this paper proposes a combined forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

2019

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The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

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Laplacian twin extreme learning machine for semi-supervised classification

Cited by 26 publications

References 23 publications

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Globality‐Locality Preserving Maximum Variance Extreme Learning Machine

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

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