scholarly journals Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1651
Author(s):  
Wenming He ◽  
Yanqing Xie ◽  
Haoxuan Lu ◽  
Mingjing Wang ◽  
Huiling Chen
Keyword(s):  
Heart Disease ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Auxiliary Function ◽  
Classification Model ◽  
Diagnostic Model ◽  
Transformation Mechanism ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Learning Machine

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.

Objective rating of fabric wrinkles via random vector functional link based on the improved salp swarm algorithm

2021 ◽  
pp. 004051752110257
Author(s):  
Zhiyu Zhou ◽  
Zijian Ma ◽  
Zefei Zhu ◽  
Yaming Wang
Keyword(s):  
Extreme Learning Machine ◽  
Random Vector ◽  
Classification Accuracy ◽  
Functional Link ◽  
Salp Swarm Algorithm ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Swarm Algorithm ◽  
Learning Machine

To solve the problem of inefficiency and inaccuracy associated with the classification of fabric wrinkles by human eyes, as well as improve current deficiencies in the application of neural networks for the classification of fabric wrinkles, we propose a model based on the salp swarm algorithm improved by ant lion optimization to optimize the random vector functional link to objectively evaluate the fabric wrinkle level. First, to improve the global searchability of the salp swarm algorithm and avoid the local optima problem, the use of ant lion optimization to improve the salp swarm algorithm is proposed in this study. Afterward, the improved salp swarm algorithm is used to optimize the input weight and hidden layer bias of the random vector functional link to avoid the inaccuracy and instability of random vector functional link classification owing to the randomness of the parameters. Finally, the performance of the proposed algorithm is verified using a fabric wrinkle dataset. Comparative experiments show that the classification accuracy of the proposed ant lion optimization - salp swarm algorithm - random vector functional link algorithm were 8.46%, 2.05%, 10.28%, 3.50%, and 4.42% higher than those of random vector functional link, improved random vector functional link based on salp swarm algorithm, extreme learning machine, improved extreme learning machine based on whale optimization algorithm, and improved backpropagation based on the Levenberg-Marquardt algorithm. Furthermore, the classification accuracy of the wrinkle level was effectively improved. All the fabrics used in this study were monochromatic, and multi-color printed fabrics have a significant impact on the difficulty of image processing and classification results. The next research step is to evaluate the wrinkle level of multi-color printed fabrics.

scholarly journals Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Zaher Mundher Yaseen ◽  
Hossam Faris ◽  
Nadhir Al-Ansari
Keyword(s):  
Extreme Learning Machine ◽  
River Flow ◽  
Forecast Accuracy ◽  
Machine Model ◽  
Salp Swarm Algorithm ◽  
Tigris River ◽  
Statistical Measures ◽  
Swarm Algorithm ◽  
Learning Machine ◽  
Intelligent Model

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

Quantum beetle swarm algorithm optimized extreme learning machine for intrusion detection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yumin Dong ◽  
Wanbin Hu ◽  
Jinlei Zhang ◽  
Min Chen ◽  
Wei Liao ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Swarm Algorithm ◽  
Learning Machine

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Wood Defect Detection Based on Depth Extreme Learning Machine

2020 ◽  
Vol 10 (21) ◽  
pp. 7488
Author(s):  
Yutu Yang ◽  
Xiaolin Zhou ◽  
Ying Liu ◽  
Zhongkang Hu ◽  
Fenglong Ding
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Defect Detection ◽  
Clustering Algorithm ◽  
Classification Performance ◽  
Image Features ◽  
Test Time ◽  
Machine Model ◽  
Feature Extraction Method ◽  
Learning Machine

The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.

A New Point-of-Interest Classification Model with an Extreme Learning Machine

2018 ◽  
Vol 10 (6) ◽  
pp. 951-964 ◽  
Author(s):  
Zhen Zhang ◽  
Xiangguo Zhao ◽  
Guoren Wang ◽  
Xin Bi
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Point Of Interest ◽  
Learning Machine

scholarly journals Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Junliang Shang ◽  
Chun-Hou Zheng
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Structural Information ◽  
Optimization Method ◽  
Classification Performance ◽  
Weight Matrix ◽  
New Method ◽  
Classification Problems ◽  
Graph Regularization ◽  
Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

Sign in / Sign up

Export Citation Format

Share Document