scholarly journals Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
C. V. Subbulakshmi ◽  
S. N. Deepa
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Learning Paradigm ◽  
Swarm Optimization ◽  
Generalization Performance ◽  
Learning Machine ◽  
Hidden Layer ◽  
Medical Dataset

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

scholarly journals Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 174 ◽  
Author(s):  
Hongli Guo ◽  
Bin Li ◽  
Wei Li ◽  
Fengjuan Qiao ◽  
Xuewen Rong ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Network Size ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Hidden Layer ◽  
Configuration Simulation

We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness.

scholarly journals Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks Based on Extreme Learning Machine Optimized by Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Li Cao ◽  
Yong Cai ◽  
Yinggao Yue
Keyword(s):  
Particle Swarm Optimization ◽  
Data Fusion ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Redundant Information ◽  
Swarm Optimization ◽  
Heterogeneous Wireless Sensor Networks ◽  
Learning Machine ◽  
Hidden Layer

Data fusion can reduce the data communication time between sensor nodes, reduce energy consumption, and prolong the lifetime of the network, making it an important research focus in the field of heterogeneous wireless sensor networks (HWSNs). Normal sensor nodes are susceptible to external environmental interferences, which affect the measurement results. In addition, raw data contain redundant information. The transmission of redundant information consumes excess energy, thereby reducing the lifetime of the network. We propose a data fusion method based on an extreme learning machine optimized by particle swarm optimization for HWSNs. The spatiotemporal correlation between the data of the HWSNs is determined, and the extreme learning machine method is used to process the data collected by the sensor nodes in the hierarchical routing structure of the HWSN. The particle swarm optimization algorithm is used to optimize the input weight matrix and the hidden layer bias of the extreme learning machine. An output weight matrix is created to reduce the number of hidden layer nodes and improve the generalization ability of the model. The data fusion model fuses the original data collected by the sensor nodes. The simulation results show that the proposed algorithm reduces network energy consumption and improves the lifetime of the network, the efficiency of data fusion, and the reliability of data transmission compared with other data fusion methods.

scholarly journals Improve Efficiency of Extreme Learning Machine based on Chaos Particle Swarm Optimization Method

2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Optimization Method ◽  
Running Speed ◽  
Swarm Optimization ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.

scholarly journals Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nico Surantha ◽  
Tri Fennia Lesmana ◽  
Sani Muhamad Isa
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Sleep Stages ◽  
Support Vector ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Portable Sensor

AbstractRecent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

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