Predictive Modeling and Sentiment Classification of Social Media Through Extreme Learning Machine
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Extreme Learning Machine
Sentiment Analysis
Categorical variable
Learning classifier system
Online machine learning
The primitive Extreme Learning Machine (ELM) [1, 2, 3] with additive neurons and RBF kernels was implemented in batch mode. In this paper, its sequential modification based on recursive least-squares (RLS) algorithm, which referred as Online Sequential Extreme Learning Machine (OS-ELM), is introduced. Based on OS-ELM, Online Sequential Fuzzy Extreme Learning Machine (Fuzzy-ELM) is also introduced to implement zero order TSK model and first order TSK model. The performance of OS-ELM and Fuzzy-ELM are evaluated and compared with other popular sequential learning algorithms, and experimental results on some real benchmark regression problems show that the proposedOnlineSequentialExtreme Learning Machine (OS-ELM) produces better generalization performance at very fast learning speed.
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The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently,with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.
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Extreme Learning Machine
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Online learning methods (OLM) have been gaining traction as a solution to classification problems because of rapid renewal and fast growth in volume of available data. ELM-based sequential learning (OS-ELM) is one of the most frequently used online learning methodologies partly due to fast training algorithm but suffers from inefficient use of its hidden layers due to the random assignment of the parameters of those layers. In this study, we propose an improved online learning model called online sequential constrained extreme learning machine (OS-CELM), which replaces the random assignment of those parameters with better generalization performance using the CELM method based on the distance between classes. We compare the performance and training times of OS-ELM, ELM, and the proposed models for four different data sets. The results indicate that the proposed model has better generalization and accuracy performance.
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Extreme learning machine is a novel single hidden layer feed-forward neural network model,whose input weights and the bias of hidden nodes are generated randomly. And its output weights are computed analytically. Consequently,the extreme learning machine owns extremely fast speed and good identification ability,which is faster than conventional BP neural network thousands times. However,the stochastic input weights and the bias of the extreme learning machine are not the best model parameters possibly when the objective function gets the global minimum value. Therefore,the least square method is adopted to seek the appropriate parameters of extreme learning machine. The improved extreme learning machine is applied to build the combustion thermal efficiency model of the plant boiler. Compared with other algorithms,such as BP,conventional extreme learning machine,particle swarm optimization extreme learning machine,teaching-learning-based optimization extreme learning machine,the result shows that the improved extreme learning machine is effective.
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The traditional Online Sequential Extreme Learning Machine(OS-ELM) has variations in different trials of simulations and the over-learning problem.Using wavelet substitutes network’s traditional activation function,structural risk minimization is used to modify the problem,so a novel algorithm called regularized wavelet extreme learning machine is proposed.Motivated by online learning method,Online Sequential Regularized Wavelet Extreme Learning Machine(OS-RWELM) is designed.Experimental results show that this algorithm avoids the local minimum and over-learning problem,has a fast online learning speed and a good generalization and robustness.
Extreme Learning Machine
Online machine learning
Structural risk minimization
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Extreme Learning Machine
Regularization
Online machine learning
Early stopping
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Extreme Learning Machine
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This paper introduces an alternative way of modeling the solar diffuse radiation based on extreme learning machine methods, which are gaining a growing interest in the scientific and research community nowadays. Several models are built that employ the classic, incremental and convex incremental extreme learning algorithm, and are compared to each other, as well as to other available models, in order to evaluate their approximation capability and accuracy. Along with the models, a few important features of the learning algorithms are discussed and alternative solutions are offered for some learning steps. Namely, the conducted research has clearly showed that the random selection of the hidden layer parameters significantly influences the approximation capacity of the classic extreme learning machine. The paper offers a simple solution to the problem. In addition, the research has confirmed that the incremental extreme learning machine indeed does not achieve the smallest possible approximation error due to the fact that the output parameters of the hidden nodes are not readjusted after the addition of each new hidden node. The paper also offers a simple solution to this problem. Finally, the convex incremental extreme learning machine tends to solve the accuracy problem of the incremental extreme learning machine. However, it still achieves smaller accuracy than the proposed solution in this paper. Nevertheless, the simulation results within this research show clearly that the extreme learning machine methods indeed possess the attributes of extreme simplicity, extremely good approximation performance, and extremely fast computation.
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Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this. It makes them more complex and computationally expensive than the standard Extreme Learning Machine model. In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense. We term our first formulation as 'Least Squared Twin Extreme Learning Machine'. It minimizes the L2-norm of error variables in its optimization problem. Our second formulation 'Weighted Linear loss Twin Extreme Learning Machine' uses the weighted linear loss function for calculating the empirical error, which makes it insensitive towards outliers. Numerical results obtained with multiple benchmark datasets show that proposed formulations are time efficient with better generalization ability. Further, we have used the proposed formulations in the detection of phishing websites and shown that they are much more effective in the detection of phishing websites than other Extreme Learning Machine models.
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Online machine learning
Empirical risk minimization
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