Clustering in extreme learning machine feature space

2014 ◽  
Vol 128 ◽  
pp. 88-95 ◽  
Author(s):  
Qing He ◽  
Xin Jin ◽  
Changying Du ◽  
Fuzhen Zhuang ◽  
Zhongzhi Shi
Keyword(s):  
Extreme Learning Machine ◽  
Feature Space ◽  
Learning Machine

scholarly journals Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Gao ◽  
Jiangang Lv
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Single Stage ◽  
Training Data ◽  
Learning Machine ◽  
Hidden Layer ◽  
Signal Processing Methods ◽  
Hidden Nodes ◽  
Selection Of

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features

2016 ◽  
Vol 14 (01) ◽  
pp. 1650006
Author(s):  
Cong Pian ◽  
Jin Zhang ◽  
Yuan-Yuan Chen ◽  
Zhi Chen ◽  
Qin Li ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Feature Space ◽  
Structural Diversity ◽  
Minimum Free Energy ◽  
Svm Classifier ◽  
Information Redundancy ◽  
Stem Loop ◽  
Training Time ◽  
Learning Machine ◽  
Total Accuracy

MicroRNAs (miRNAs) are a set of short (21–24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. Therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). These features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). The results show that the specificity ([Formula: see text])and sensitivity ([Formula: see text]) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.

scholarly journals Distributed Learning over Massive XML Documents in ELM Feature Space

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xin Bi ◽  
Xiangguo Zhao ◽  
Guoren Wang ◽  
Zhen Zhang ◽  
Shuang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithms ◽  
Distributed Learning ◽  
Feature Space ◽  
Information Representation ◽  
Xml Data ◽  
Xml Documents ◽  
Training Samples ◽  
Effectiveness And Efficiency ◽  
Learning Machine

With the exponentially increasing volume of XML data, centralized learning solutions are unable to meet the requirements of mining applications with massive training samples. In this paper, a solution to distributed learning over massive XML documents is proposed, which provides distributed conversion of XML documents into representation model in parallel based on MapReduce and a distributed learning component based on Extreme Learning Machine for mining tasks of classification or clustering. Within this framework, training samples are converted from raw XML datasets with better efficiency and information representation ability and taken to distributed learning algorithms in Extreme Learning Machine (ELM) feature space. Extensive experiments are conducted on massive XML documents datasets to verify the effectiveness and efficiency for both classification and clustering applications.

A Feature Weighted Kernel Extreme Learning Machine Ensemble Method for Gas Turbine Fault Diagnosis

2020 ◽  
Author(s):  
Liping Yan ◽  
Xuezhi Dong ◽  
Hualiang Zhang ◽  
Haisheng Chen
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Extreme Learning Machine ◽  
Information Gain ◽  
Feature Space ◽  
Ensemble Method ◽  
Separating Hyperplane ◽  
Weighted Kernel ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Abstract Fault diagnosis is a very important section of gas turbine maintenance. Kernel extreme learning machine (KELM), a novel artificial intelligence algorithm, is a potentially effective diagnosis technology. The existing KELMs are all assumed that there is the same influence to the optimal separating hyperplane from all features, which reduces its generalization performance. In this study, a feature weighted kernel extreme learning machine ensemble method (FWKELM-RF) is developed for application in the field of gas turbine fault diagnosis. First, information gain ratio is introduced to assign different weights to the feature space. Furthermore, random forest is used to enhance stable performance of feature weighted KELM. The fault datasets from a gas turbine with three shafts is generated to validate the performance of the developed method, and the results demonstrate that FWKELM-RF can achieve better accuracy and stability for detecting fault in gas turbine.

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