scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

2020 ◽  
Vol 62 (1) ◽  
pp. 15-21
Author(s):  
Changdong Wu
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Activation Function ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

scholarly journals An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

2021 ◽  
Vol 38 (4) ◽  
pp. 1229-1235
Author(s):  
Derya Avci ◽  
Eser Sert
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Optical Illusions ◽  
Factors Affecting ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

scholarly journals Evolutionary Voting-Based Extreme Learning Machines

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nan Liu ◽  
Jiuwen Cao ◽  
Zhiping Lin ◽  
Pin Pin Pek ◽  
Zhi Xiong Koh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Extreme Learning Machine ◽  
Real World ◽  
Classification Accuracy ◽  
Majority Voting ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Learning Machine ◽  
Elm Classifier

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data

2014 ◽  
Vol 875-877 ◽  
pp. 2020-2024 ◽  
Author(s):  
Yan Shi ◽  
Li Jie Zhao ◽  
Jian Tang
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Near Infrared ◽  
High Dimensional Data ◽  
Disease Diagnosis ◽  
Mass Spectrometric ◽  
High Dimensional ◽  
Learning Speed ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

High dimensional data such as mass-spectrometric and near-infrared spectrum are always used in disease diagnosis and product quality monitoring. Aim at the nonlinear feature extraction and low learning speed problems, a novel modeling approach combined principal component analysis (PCA) with kernel extreme learning machine (KELM) is proposed. The extracted features using PCA algorithms are fed into nonlinear classification based KELM with fast learning speed. The numbers of the features are selected according the classification performance. The experimental results based on the mass-spectrometric data in the benchmark demonstrate that the proposed approach has better performance. This approach can also be used to target recognition based on radar data.

Improving Classification Accuracy Using Hybrid of Extreme Learning Machine and Artificial Algae Algorithm with Multi-Light Source

2020 ◽  
Vol 28 (02) ◽  
pp. 213-236
Author(s):  
Devikanniga D ◽  
R. Joshua Samuel Raj
Keyword(s):  
Extreme Learning Machine ◽  
Light Source ◽  
Classification Accuracy ◽  
Optimization Technique ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Rank Test ◽  
Medical Domain ◽  
Learning Machine ◽  
Hidden Neurons

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers.

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