A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

Computational Intelligence and Neuroscience ◽

10.1155/2015/405890 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Jie Wang ◽

Liangjian Cai ◽

Jinzhu Peng ◽

Yuheng Jia

Keyword(s):

Extreme Learning Machine ◽

Multiple Instance Learning ◽

Data Sets ◽

Feedforward Network ◽

Real World Data ◽

Benchmark Data ◽

Input And Output ◽

Learning Machine ◽

Hidden Layer

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.

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Multiple-Instance Learning via an RBF Kernel-Based Extreme Learning Machine

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0011 ◽

2017 ◽

Vol 26 (1) ◽

pp. 185-195 ◽

Cited By ~ 3

Author(s):

Jie Wang ◽

Liangjian Cai ◽

Xin Zhao

Keyword(s):

Extreme Learning Machine ◽

State Of The Art ◽

Multiple Instance Learning ◽

Training Data ◽

Data Sets ◽

Rbf Kernel ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Hidden Layer ◽

Instance Space

AbstractAs we are usually confronted with a large instance space for real-word data sets, it is significant to develop a useful and efficient multiple-instance learning (MIL) algorithm. MIL, where training data are prepared in the form of labeled bags rather than labeled instances, is a variant of supervised learning. This paper presents a novel MIL algorithm for an extreme learning machine called MI-ELM. A radial basis kernel extreme learning machine is adapted to approach the MIL problem using Hausdorff distance to measure the distance between the bags. The clusters in the hidden layer are composed of bags that are randomly generated. Because we do not need to tune the parameters for the hidden layer, MI-ELM can learn very fast. The experimental results on classifications and multiple-instance regression data sets demonstrate that the MI-ELM is useful and efficient as compared to the state-of-the-art algorithms.

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Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System

Biomedical Computing for Breast Cancer Detection and Diagnosis - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-7998-3456-4.ch003 ◽

2021 ◽

pp. 28-46

Author(s):

Maíra Araújo de Santana ◽

Jessiane Mônica Silva Pereira ◽

Clarisse Lins de Lima ◽

Maria Beatriz Jacinto de Almeida ◽

José Filipe Silva de Andrade ◽

...

Keyword(s):

Extreme Learning Machine ◽

Kernel Function ◽

Intelligent System ◽

Classification Problem ◽

Features Extraction ◽

Breast Lesions ◽

Maximum Accuracy ◽

Learning Machine ◽

Hidden Layer

This study aims to assess the breast lesions classification in thermographic images using different configuration of an Extreme Learning Machine network as classifier. In this approach, the authors changed the number of neurons in the hidden layer and the type of kernel function to further explore the network in order to find a better solution for the classification problem. Authors also used different tools to perform features extraction to assess both texture and geometry information from the breast lesions. During the study, the authors found that the results changed not only due to the network parameters but also due to the features chosen to represent the thermographic images. A maximum accuracy of 95% was found for the differentiation of breast lesions.

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

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2020.62.1.15 ◽

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.

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EVOLVING EXTREME LEARNING MACHINE PARADIGM WITH ADAPTIVE OPERATOR SELECTION AND PARAMETER CONTROL

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488513400229 ◽

2013 ◽

Vol 21 (supp02) ◽

pp. 143-154 ◽

Cited By ~ 10

Author(s):

KE LI ◽

RAN WANG ◽

SAM KWONG ◽

JINGJING CAO

Keyword(s):

Extreme Learning Machine ◽

Empirical Studies ◽

Data Sets ◽

Classification Problems ◽

Generalization Capability ◽

Adaptive Operator Selection ◽

Recent Classification ◽

Learning Machine ◽

Hidden Layer ◽

Offspring Generation

Extreme Learning Machine (ELM) is an emergent technique for training Single-hidden Layer Feedforward Networks (SLFNs). It attracts significant interest during the recent years, but the randomly assigned network parameters might cause high learning risks. This fact motivates our idea in this paper to propose an evolving ELM paradigm for classification problems. In this paradigm, a Differential Evolution (DE) variant, which can online select the appropriate operator for offspring generation and adaptively adjust the corresponding control parameters, is proposed for optimizing the network. In addition, a 5-fold cross validation is adopted in the fitness assignment procedure, for improving the generalization capability. Empirical studies on several real-world classification data sets have demonstrated that the evolving ELM paradigm can generally outperform the original ELM as well as several recent classification algorithms.

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Exploring the Extreme Learning Machine for Classification of Brain MRIs

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1909.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3654-3657

Keyword(s):

Extreme Learning Machine ◽

Principal Component ◽

Classification Performance ◽

Feature Reduction ◽

Brain Diseases ◽

Discrete Wavelet ◽

Learning Machine ◽

Hidden Layer ◽

Method Accuracy

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

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AN IMPROVEMENT OF EXTREME LEARNING MACHINE FOR COMPACT SINGLE-HIDDEN-LAYER FEEDFORWARD NEURAL NETWORKS

International Journal of Neural Systems ◽

10.1142/s0129065708001695 ◽

2008 ◽

Vol 18 (05) ◽

pp. 433-441 ◽

Cited By ~ 51

Author(s):

HIEU TRUNG HUYNH ◽

YONGGWAN WON ◽

JUNG-JA KIM

Keyword(s):

Neural Networks ◽

Extreme Learning Machine ◽

High Speed ◽

Learning Algorithm ◽

Differential Evolution Algorithm ◽

Feedforward Neural Networks ◽

Analytical Determination ◽

Data Sets ◽

Learning Machine ◽

Hidden Layer

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing.

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Classificação de Espécies de Plantas Usando Extreme Learning Machine

10.5753/eniac.2019.9268 ◽

2019 ◽

Author(s):

Larissa Britto ◽

Luciano Pacífico

Keyword(s):

Extreme Learning Machine ◽

Plant Species ◽

Hypothesis Test ◽

Species Recognition ◽

General Purpose ◽

Statistical Hypothesis ◽

Data Sets ◽

Real World Data ◽

Statistical Hypothesis Test ◽

Learning Machine

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

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User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096512666191004130655 ◽

2020 ◽

Vol 13 (5) ◽

pp. 641-649

Author(s):

Yuancheng Li ◽

Yaqi Cui ◽

Xiaolong Zhang

Keyword(s):

Extreme Learning Machine ◽

Clustering Algorithm ◽

Characteristic Curve ◽

Clustering Algorithms ◽

Data Sets ◽

Residential Areas ◽

Processing Power ◽

Learning Machine ◽

Advanced Metering ◽

Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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