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نتیجه جستجو - Extreme Learning Machines

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
شبکه های هیدروکربنی مصنوعی موازی تصادفی تصادفی: پیاده سازی برای یادگیری ماشین تحت نظارت سریع و قوی در داده های با ابعاد بالا-2020
Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10, 000???? times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPEAHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential.
Keywords: Machine learning | Parallel computing | Extreme learning machines | Stochastic learning | Regression | Classification | Big data
مقاله انگلیسی
2 Comparing of deep neural networks and extreme learning machines based on growing and pruning approach
مقایسه شبکه های عصبی عمیق و دستگاههای یادگیری افراطی بر اساس رویکرد در حال رشد و هرس-2020
Recently, the studies based on Deep Neural Networks and Extreme Learning Machines have become prominent. The models of parameters designed in these studies have been chosen randomly and the models have been designed in this direction. The main focus of this study is to determine the ideal pa- rameters i.e. optimum hidden layer number, optimum hidden neuron number and activation function for Deep Neural Networks and Extreme Learning Machines architectures based on growing and pruning ap- proach and to compare the performances of the models designed. The performances of the models are evaluated on two datasets; Parkinson and Self-Care Activities Dataset. Multi experiments have verified that the Deep Neural Networks architectures present a good prediction performance and this architec- ture outperforms the Extreme Learning Machines.
Keywords: Deep Neural Networks | Extreme Learning Machines | Growing and pruning | Parkinson | Self-care activities
مقاله انگلیسی
3 Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification
درختان عصبی با دانش همتا به همتا و سرور به مشتری انتقال مدل برای طبقه بندی داده های بعدی-2019
Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner- redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node. The derived classification rules from ELM are stored in the rule- base of the inference engine to recognize the classes. A majority voting is used to unify the results of the different neural trees. This structure is refereed as the Forest of Extreme Learning Machines with Rule- base Transferring (FELM-RT). The contribution of FELM-RT is to decrease the duplicated computations by using two novel interaction models between the neural trees. In the first interaction model, namely Peer- to-Peer (P2P) model, each node can share its rule-base with the other nodes of the various neural trees. In the second that is referred as Server-to-Client (S2C) model, a neural tree that works on a cluster with the best relevancy and redundancy, shares the rules with the other neural trees. In both of the models, a fuzzy aggregation technique is used to adjust the certainty of the rules. The processing time of FELM-RT decreases essentially and it improves the classification accuracy. The high results of F-measure and G- mean, show that FELM-RT classifies the high-dimensional datasets without over-fitting. The comparison between FELM-RT and some state-of-the-art classifiers reveals that FELM-RT overcomes them specially on the datasets with more than 3 million features.
Keywords: Neural tree | Rule-base transferring | Feature clustering | Extreme learning machine | Communication models
مقاله انگلیسی
4 Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems
ماشینهای یادگیری افراطی دو فاز با ترکیب کامل حالت تجربی گروه با الگوریتم نویز تطبیقی برای مشکلات پیش بینی رواناب چند مقیاسی-2019
Expert systems adopted in real-time multi-scale runoff prediction are useful decision-making tools for hydrologists but the stochastic nature of any hydrological variable can pose significant challenges in attaining a reliable predictive model. This paper advocates a data-driven approach used to design two-phase hybrid model (i.e., CVEE-ELM). The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) coupled with the variational mode decomposition (VMD) algorithms for better frequency resolution of the input datasets and the extreme learning machine (ELM) algorithm as the objective predictive model. In the first stage of the presented model design, notable frequencies in the predictor-target data series are uncovered, utilizing the CEEMDAN algorithm where the model’s inputs are decomposed into their respective Intrinsic Mode Functions (IMFs) and the Residual (Res) series. The second stage entails a VMD approach, used to decompose the yet-unresolved high frequencies (i.e., IMF1) into their variational modes, further discerning and establishing the data attributes to be incorporated into the ELM model to simulate the respective IMFs, Res and VM data series, aggregated as an integrative tool for multiscale runoff prediction. In the model evaluative phase, the hybrid CVEE-ELM is cross-validated with a single-phase hybrid ELM and an autoregressive integrated moving average (ARIMA) model to benchmark its accuracy for predicting 1-, 3- and 6-month ahead runoff in Yingluoxia watershed, Northwestern China. Two-phase hybrid model exhibits superior accuracy at all lead times, to accord with high degree of correlations between the observed and the forecasted runoff, a relatively large Nash-Sutcliffe and the Legate-McCabe Index. Taylor diagrams depict the two-phase hybrid CVEE-ELM model generated forecasts located close to a reference (i.e., a perfect) model, with a lower root-mean square centered difference, and a correspondingly large correlation for all forecast horizons, although the model’s accuracy for shorter lead times (1-month), as expected, are better than the 3- and 6-month horizon. The study shows that the two-phase hybrid CVEE-ELM model where an integration of two frequency resolution algorithms are made, is a preferred datadriven tool that can be explored for real-life decision-system design, particularly for hydrological forecasting problems that have significantly stochastic data features, and thus, will require reliable forecasts to be generated at multi-step horizons
Keywords: Expert system | Runoff | Integrated model | Complete ensemble empirical mode decomposition | adaptive noise (CEEMDAN) | Variational mode decomposition (VMD) | Extreme learning machine (ELM)
مقاله انگلیسی
5 An expert system for predicting the velocity field in narrow open channel flows using self-adaptive extreme learning machines
یک سیستم خبره برای پیش بینی میدان سرعت در جریان کانال باریک باریک با استفاده از ماشینهای یادگیری افراطی خود سازگار-2019
This work investigates the ability of a new model based on powerful self-adaptive extreme learning machines to predict the velocity field in an open channel. A total of 363 velocity field data obtained in 8 different hydraulic conditions of a narrow open channel are used to develop the proposed model. The performance of the proposed model in predicting the velocity field is analysed for 3 different discharge rates that have no role in model development. According to the model prediction accuracy comparisons, the proposed model is more accurate than existing equations and can be employed successfully in velocity field predicting. Furthermore, the new model can more accurately predict the negative gradient of velocity near the free surface, which is the most significant/complex feature of a velocity distribution in narrow open channels. Moreover, a sensitivity analysis is done to surrey the effect of the proposed model on each input variable.
Keywords: Discharge | Field data | Flow velocity profile | Open channel | Sensitivity analysis | Velocity
مقاله انگلیسی
6 Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر قطار: چشم انداز تحلیل داده های بزرگ-2018
Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
Keywords: Railway network ، Train Delay Prediction systems ، Big data analytics ، Extreme learning machines ، Shallow architecture ، Deep architecture
مقاله انگلیسی
7 Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر در قطار: چشم انداز تجزیه و تحلیل داده بزرگ-2017
Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
Keywords: Railway network | Train Delay Prediction systems | Big data analytics | Extreme learning machines | Shallow architecture | Deep architecture
مقاله انگلیسی
8 Ensemble of Extreme Learning Machines with Trained Classifier Combination and Statistical Features for Hyperspectral Data
اثر کلی از ماشین های یادگیری وسیع با ترکیب مرتب آموزش یافته و ویژگی های آماری برای داده های Hyperspectral-2017
Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper we introduce a novel pipeline for feature extraction and classification of hyperspectral images. To obtain a compressed representation we propose to extract a set of statistical-based properties from these images. This allows for embedding feature space into fourteen channels, obtaining a significant dimensionality reduction. These features are used as an input for the ensemble learning based on randomized neural networks. We introduce a novel method for forming ensembles of extreme learning machines based on randomized feature subspaces and a trained combiner. It is based on continuous outputs and uses a perceptron- based learning scheme to calculate weights assigned to each classifier and class independently. Extensive experiments carried on a number of benchmarks images prove that using proposed feature extraction and extreme learning ensemble leads to a significant gain in classification accuracy.
Keywords: Ensemble learning | Extreme learning machines | Hyperspectral imaging | Computer vision | Feature extraction | Dimensionality reduction
مقاله انگلیسی
9 Comparison of combining methods using Extreme Learning Machines under small sample scenario
مقایسه روش های ترکیبی با استفاده از ماشین یادگیری نهایی تحت سناریوی نمونه های کوچک-2016
Making accurate predictions is a difficult task that is encountered throughout many research domains. In certain cases, the number of available samples is so scarce that providing reliable estimates is a challenging problem. In this paper, we are interested in giving as accurate predictions as possible based on the Extreme Learning Machine type of a neural network in small sample data scenarios. Most of the Extreme Learning Machine literature is focused on choosing a particular model from a pool of candidates, but such approach usually ignores model selection uncertainty and has inferior performance compared to combining methods. We empirically examine several model selection criteria coupled with new model combining approaches that were recently proposed. The results obtained indicate that a careful choice among the combinations must be performed in order to have the most accurate and stable predictions.& 2015 Elsevier B.V. All rights reserved.
Keywords: Extreme Learning Machine | Small sample data | Model selection | Model combining | Mallows Model Averaging | Jackknife Model Averaging
مقاله انگلیسی
10 Mining of protein–protein interfacial residues from massive protein sequential and spatial data
استخراج پروتئین- پروتئین باقی مانده سطحی از پروتئین های پی در پی گسترده و داده های مکانی-2015
It is a great challenge to process big data in bioinformatics. In this paper, we addressed the problem of identifying protein–protein interfacial residues from massive protein structural data. A protein set, comprising 154 993 residues, was analyzed. We applied the three-dimensional alpha shape modeling to the search of surface and interfacial residues in this set, and adopted the spatially neigh- boring residue profiles to characterize each residue. These residue profiles, which revealed the sequential and spatial information of proteins, translated the original data into a large matrix. After vertically and horizontally refining this matrix, we comparably implemented a series of popular learning procedures, including neuro-fuzzy classifiers (NFCs), CART, neighborhood classifiers (NECs), extreme learning machines (ELMs) and naive Bayesian classifiers (NBCs), to predict the interfacial residues, aiming to investigate the sensitivity of these massive structural data to different learning mechanisms. As a consequence, ELMs, CART and NFCs performed better in terms of computational costs; NFCs, NBCs and ELMs provided favorable prediction accuracies. Overall, NFCs, NBCs and ELMs are favourable choices for fastly and accurately handling this type of data. More importantly, the marginal differences between the prediction performances of these methods imply the insensitivity of this type of data to different learning mechanisms.
Keywords: Protein–protein interface prediction | 3D alpha shape modeling | Residue sequence profile | Joint mutual information (JMI) | Neuro-fuzzy classifiers (NFCs) | Neighborhood classifiers (NECs) | CART | Extreme learning machines (ELMs) | Naive Bayesian classifiers (NBCs)
مقاله انگلیسی
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