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.

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

Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

2019 ◽  
Vol 14 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Ji-Yong An ◽  
Yong Zhou ◽  
Lei Zhang ◽  
Qiang Niu ◽  
Da-Fu Wang
Keyword(s):  
Extreme Learning Machine ◽  
Functional Organization ◽  
Web Server ◽  
Feature Representation ◽  
Experimental Results ◽  
Evolutionary Information ◽  
Svm Classifier ◽  
Interacting Proteins ◽  
Learning Machine ◽  
Human Dataset

Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell. Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation. Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. </P><P> Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods. Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

scholarly journals A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1284
Author(s):  
Licheng Cui ◽  
Huawei Zhai ◽  
Hongfei Lin
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Weight Matrix ◽  
Testing Time ◽  
Least Squares Regression ◽  
Classification Problems ◽  
Training Time ◽  
Discrimination Ability ◽  
Learning Machine ◽  
Hidden Layer

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.

scholarly journals Incremental Learning for Classification of Unstructured Data Using Extreme Learning Machine

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 158 ◽  
Author(s):  
Sathya Madhusudhanan ◽  
Suresh Jaganathan ◽  
Jayashree L S
Keyword(s):  
Extreme Learning Machine ◽  
Incremental Learning ◽  
Streaming Data ◽  
Unstructured Data ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Class Labels ◽  
Learning Machine ◽  
Over Time

Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.

scholarly journals Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141879299 ◽  
Author(s):  
Zhiyu Zhou ◽  
Hanxuan Guo ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Computational Time ◽  
Improved Genetic Algorithm ◽  
Intelligent Algorithm ◽  
End Effector ◽  
Training Time ◽  
Learning Machine

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

scholarly journals Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xingshuo An ◽  
Xianwei Zhou ◽  
Xing Lü ◽  
Fuhong Lin ◽  
Lei Yang
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Detection System ◽  
Fog Computing ◽  
Training Data ◽  
Experimental Simulation ◽  
Detection Accuracy ◽  
New Paradigm ◽  
Training Time ◽  
Learning Machine

Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.

scholarly journals Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3555
Author(s):  
Wei Zhou ◽  
Wei Wang ◽  
De Zhao
Keyword(s):  
Extreme Learning Machine ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Intelligent Transport System ◽  
Training Time ◽  
Passenger Flow ◽  
Singular Spectrum ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

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