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检索条件"任意字段=19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018"

共 134 条记录，以下是51-60 订阅

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Detecting Intrusive Malware with a Hybrid Generative Deep learning Model 19th

Detecting Intrusive Malware with a Hybrid Generative Deep Le...

引用

19th international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

A small amount of unknown malware can be analyzed manually, but it is generated with extremely more and more so that automatic detection of them is needed. Malware is usually generated with different features from those of existing ones (e.g., code exchange, null value insertion, or reorganization of subroutines) to avoid detection of antivirus systems. To detect malware with obfuscation, this paper proposes a method called latent semantic controlling generative adversarial networks (LSC-GAN) that learns to generate malware data with i-feature from a specific Gaussian distribution which represents ifeature and distinguish it from the real. Variational autoencoder (VAE) projects data to latent space for feature extraction and is transferred to generator (G) of LSC-GAN to train it stably. G generates data from Gaussian distribution, so it produces similar data but not identical to the actual data: it includes modified features compared with the real. the detector is inherited with transfer learning in a encoder that learns various malware features using real and modified data generated by the LSC-GAN based on a LSC-VAE. We show that LSC-GAN achieves detection accuracy of 96.97% on average that is higher than those of other conventional models. We demonstrate statistical significance of the performance of the proposed model using t-test. the result of detection is analyzed with confusion matrix and F1-score.

关键词： Malicious software Malware detection Generative adversarial networks Variational autoencoder Transfer learning

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data Pre-processing to Apply Multiple Imputation Techniques: A Case Study on Real-World Census data 19th

Data Pre-processing to Apply Multiple Imputation Techniques:...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Ruiz-Chavez, Zoila Salvador-Meneses, Jaime Garcia-Rodriguez, Jose Tallon-Ballesteros, Antonio J. Univ Cent Ecuador Ciudadela Univ Quito Ecuador Univ Alicante Ap 99 Alicante 03080 Spain Univ Seville Seville Spain

ISBN: (纸本)9783030034962;9783030034955

Improving accuracy or reducing computational cost are the main approaches of machine learning techniques, but it depends heavily on the test data used. Even more so when it comes to from real-world data such as censuses, surveys or tokens that contain a high level of missing values. the data absence or presence of outliers are problems that must be treated carefully prior to any process related to data analysis. the following work presents an overview of data pre-processing and aims at presenting the steps to follow prior to process large volumes of high-dimensionality data with categorical variables. As part of the dimensionality reduction process, when there is a high level of missing values present in one or more variables, we use the Pairwise and Listwise Deletion methods. thus, the generation of m-clusters using the Kohonen Self-Organizing Maps (SOM) algorithm with H2O over R is also considered as a division of data into similar groups, which are used as cluster to apply Multiple Imputation algorithms, creating different m-values to impute a missing value.

关键词： Machine learning data preparation data mining Imputation methods

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General Structure Preserving Network Embedding 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhu, Sinan Jia, Caiyan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Network embedding has attracted increasing attention in recent years since it represents large scale networks in low-dimensional space and provides an easier way to analysis networks. Existing embedding methods either focus on preserving the microscopic topology structure, or incorporate the mesoscopic community structure of a network. However, in the real world, a network may not only contain community structure, but have bipartite-structure, star-structure or other general structures, where nodes in each cluster have similar patterns of connections to other nodes. Empirically, general structure is important for describing the features of networks. In this paper, based on nonnegative matrix factorization framework, we propose GS-NMF which is capable of integrating topology structure and general structure into embedding process. the experimental results show that GS-NMF overcomes the limitation of previous methods and achieves obvious improvement on node clustering, node classification, and visualization.

关键词： Network embedding General structure Nonnegative matrix factorization

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Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Gomez-Pulido, Juan A. Cortes-Toro, Enrique Duran-Dominguez, Arturo Crawford, Broderick Soto, Ricardo Univ Extremadura Caceres 10003 Spain Univ Playa Ancha Valparaiso 2340000 Chile Pontificia Univ Catolica Valparaiso Valparaiso 2374631 Chile

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

the prediction of the students' performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. this collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. the best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. the results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.

关键词： Metaheuristics Genetic algorithms Vapour-liquid equilibrium Recommender systems Prediction Matrix factorization Stochastic gradient descent learning rate Regularization factor

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Inferring Temporal Structure from Predictability in Bumblebee learning Flight 19th

Inferring Temporal Structure from Predictability in Bumblebe...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Meyer, Stefan Bertrand, Olivier J. N. Egelhaaf, Martin Hammer, Barbara Univ Bielefeld Dept Neurobiol Univ Str 25 D-33615 Bielefeld Germany Univ Bielefeld Dept Machine Learning Inspirat 1 D-33619 Bielefeld Germany

ISBN: (纸本)9783030034931;9783030034924

Insects are succeeding in remarkable navigational tasks. Bumblebees, for example, are capable of learning their nest location with sophisticated flight manoeuvres, forming a so-called learning flight. the learning flights - thought to be partially pre-programmed - enable the bumblebee to memorise spatial relations between its inconspicuous nest entrance and environmental cues. To date, environmental features (e.g. object positions on the eyes) and learning experience of the insect were used to describe the flights, but its structure, thought to facilitated learning, has not been investigated systematically. In this work, we present a novel approach, to examine whether and in which time span flight behaviour is predictable based on intrinsic properties only rather than external sensory information. We study the temporal composition of learning flights by estimating the smoothness of the underlying process. We then use echo state networks (ESN) and linear models (ARIMA) to predict the bumblebee trajectory from its past motion and identify different time-scales in learning flights using their prediction-power. We found that direct visual information is not necessary within a 200ms time-window to explain the bumblebee behaviour during its learning flight.

关键词： Time series prediction Modelling Echo state network learning flight Bumblebee

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Merging ELMs with Satellite data and Clear-Sky Models for Effective Solar Radiation Estimation 19th

Merging ELMs with Satellite Data and Clear-Sky Models for Ef...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Cornejo-Bueno, L. Casanova-Mateo, C. Sanz-Justo, J. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Valladolid LATUV Lab Teledetecc Valladolid Spain

ISBN: (纸本)9783030034962;9783030034955

this paper proposes a new approach to estimate Global Solar Radiation based on the use of the Extreme learning Machine (ELM) technique combined with satellite data and a clear-sky model. Our study area is the radiometric station of Toledo, Spain. In order to train the Neural Network proposed, one complete year of hourly global solar radiation data (from the 1st of May 2013 to the 30th of April 2014) is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the ELM we have selected as a reference a physical-based method which considers the relation between a clear-sky index and a cloud cover index. then a measure of the Root Mean Square Error (RMSE) and the Pearson's Correlation Coefficient (r(2)) is obtained to evaluate the performance of the suggested methodology against the reference model. We show the improvement of the results obtained by the ELM with respect to those obtained by the physical-based method considered.

关键词： Solar radiation estimation Extreme learning machines Meteosat data

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Predicting the Household Power Consumption Using CNN-LSTM Hybrid Networks 19th

Predicting the Household Power Consumption Using CNN-LSTM Hy...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Tae-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

Prediction of power consumption is an integral part of the operation and planning of the electricity supply company. In terms of power supply and demand, For the stable supply of electricity, the reserve power must be prepared. However, it is necessary to predict electricity demand because electricity is difficult to store. In this paper, we propose a CNN-LSTM hybrid network that can extract spatio-temporal information to effectively predict the house power consumption. Experiments have shown that CNN-LSTM hybrid networks, which linearly combine convolutional neural network (CNN), long short-term memory (LSTM) and deep neural network (DNN), can extract irregular features of electric power consumption. the CNN layer is used to reduce the spectrum of spatial information, the LSTM layer is suitable for modeling temporal information, the DNN layer generates a predicted time series. the CNN-LSTM hybrid approach almost completely predicts power consumption. Finally, the CNN-LSTM hybrid method achieves higher root mean square error (RMSE) than traditional predictive methods for the individual household power consumption data sets provided by the UCI repository.

关键词： Power consumption prediction Deep learning Convolutional neural network Long short-term memory

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A First Approach to Face Dimensionality Reduction through Denoising Autoencoders 19th

A First Approach to Face Dimensionality Reduction Through De...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Pulgar, Francisco J. Charte, Francisco Rivera, Antonio J. del Jesus, Maria J. Univ Jaen Dept Comp Sci Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain

ISBN: (纸本)9783030034931;9783030034924

the problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. there are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged. In this work, denoising autoencoders (DAEs) are used to reduce dimensionality. To verify its performance, an experimentation is carried out where the improvement obtained with different types of classifiers is verified. the classification method used are: kNN, SVM, C4.5 and MLP. the test for kNN and SVM show a better predictive performance for all datasets. the executions for C4.5 and MLP reflect improvements only in some cases. the execution time is lower for all tests. In addition, a comparison between DAEs and PCA, a classical method of dimensionality reduction, is performed, obtaining better results with DAEs in most cases. the conclusions reached open up new lines of future work.

关键词： Classification Deep learning Autoencoders Denoising autoencoders Dimensionality reduction High dimensionality

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Framework for the Training of Deep Neural Networks in TensorFlow Using Metaheuristics 19th

Framework for the Training of Deep Neural Networks in Tensor...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Munoz-Ordonez, Julian Cobos, Carlos Mendoza, Martha Herrera-Viedma, Enrique Herrera, Francisco Tabik, Siham Univ Cauca Informat Technol Res Grp GTI Popayan Colombia Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783030034931;9783030034924

Artificial neural networks (ANN) again are playing a leading role in machine learning, especially in classification and regression processes, due to the emergence of deep learning (ANNs with more than four hidden layers), allowing them to encode more and more complex features. the increase in the number of hidden layers in ANNs has posed important challenges in their training. Variations (e.g. RMSProp) of classical algorithms such as backpropagation with its stochastic gradient descent are the state of the art for training deep ANNs. However, other research has shown that the advantages of metaheuristics need more detailed study in this area. We summarize the design and use of a framework to optimize learning of deep neural networks in TensorFlow using metaheuristics, a framework implemented in Python that allows training of the networks in CPU or GPU depending on the TensorFlow configuration and allows easy integration of diverse classification and regression problems solved with different neural networks architectures (conventional, convolutional and recurrent) and new metaheuristics. the framework initially includes Particle Swarm Optimization, Global-best Harmony Search, and Differential Evolution. It further enables the conversion of metaheuristics into memetic algorithms including exploitation processes using the algorithms available in TensorFlow: RMSProp, Adam, Adadelta, Momentum, and Adagrad.

关键词： Deep learning Neuroevolution TensorFlow Metaheuristic Framework

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Machine learning for Drugs Prescription 19th

Machine Learning for Drugs Prescription

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Silva, P. Rivolli, A. Rocha, P. Correia, F. Soares, C. Univ Porto Sch Engn Porto Portugal Fed Univ Technol Apucarana Parana Brazil Glintt Healthcare Solut Sa Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

In a medical appointment, patient information, including past exams, is analyzed in order to define a diagnosis. this process is prone to errors, since there may be many possible diagnoses. this analysis is very dependent on the experience of the doctor. Even with the correct diagnosis, prescribing medicines can be a problem, because there are multiple drugs for each disease and some may not be used due to allergies or high cost. therefore, it would be helpful, if the doctors were able to use a system that, for each diagnosis, provided a list of the most suitable medicines. Our approach is to support the physician in this process. Rather than trying to predict the medicine, we aim to, given the available information, predict the set of the most likely drugs. the prescription problem may be solved as a Multi-Label classification problem since, for each diagnosis, multiple drugs may be prescribed at the same time. Due to its complexity, some simplifications were performed for the problem to be treatable. So, multiple approaches were done with different assumptions. the data supplied was also complex, with important problems in its quality, that led to a strong investment in data preparation, in particular, feature engineering. Overall, the results in each scenario are good with performances almost twice the baseline, especially using Binary Relevance as transformation approach.

关键词： Machine learning Classification problems Multi-label classification Health systems Medicines prescriptions

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