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IEEE International Conference on Fuzzy Systems

作者： Jose G. M. Esgario Renato A. Krohling Graduate Program in Computer Science PPGI Federal University of Espírito Santo – UFES Vitória ES Brasil Production Engineering Department LABCIN & Graduate Program in Computer Science PPGI Federal University of Espírito Santo - UFES Vitória ES Brasil

Clustering is the process of grouping similar objects into the same partition and keeping dissimilar objects on different partitions. Clustering algorithms based on Minimum Spanning Tree (MST) have been successfully applied in the separation of non-convex clusters, although the use of individual objective functions limits the algorithms to a reduced set of clustering problems, presenting difficulties in cases of unbalanced, noisy, overlapping datasets, etc. In order to make clustering process more robust, this paper proposes an algorithm based on Minimum Spanning Tree that combines different objective functions using TOPSIS. The algorithm performance was evaluated on real and synthetic datasets. Experimental results indicate that the combination of objective functions improves clustering results compared to individual functions.

关键词： Clustering algorithms Linear programming Partitioning algorithms Euclidean distance Optimization Mutual information computer science

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An approach to improve online sequential extreme learning machines using restricted Boltzmann machines

An approach to improve online sequential extreme learning ma...

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International Joint Conference on Neural Networks

作者： Andre G. C. Pacheco Renato A. Krohling Graduate program in computer science PPGI Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil Graduate program in computer science PPGI and Production engineering department Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil

The extreme learning machine (ELM) is an analytical algorithm developed to train single-hidden layer feedforward neural network (SLFN). Although the ELM provides a good generalization and a very fast training phase, two shortcomings can be pointed in this approach: it assigns its input weights randomly and it is not able to handle sequential learning. Thereby, the online sequential extreme learning machine (OS-ELM) was proposed in order to tackle the lack of sequential learning on ELMs. Nonetheless, the OS-ELM still assigns the input weights randomly. Thus, in this work, we proposed an approach based on a restricted Boltzmann machine (RBM) to improve the OS-ELM by determining its input weights throughout the training phase. We applied our method to several well-known benchmarks and the results show that the proposed approach is able to improve the OS-ELM for most them.

关键词： Training Training data Neurons Standards Feedforward neural networks Arrays

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A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust

arXiv

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arXiv 2019年

作者： Manso, Giuliano L. Knidel, Helder Krohling, Renato A. Ventura, José A. Labcin-Laboratory of Computing and Engineering Inspired by Nature UFES-Federal University of Espirito Santo Vitória Brazil PPGI-Graduate Program in Computer Science UFES Vitória Brazil Production Engineering Department UFES Vitória Brazil Incaper Rua Afonso Sarlo Vitória ES29052-010 Brazil

Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and leaf miner Leucoptera coffeella. The progression of the diseases and or pests occurs spatially and temporarily. So, it is very important to automatically identify the degree of severity. The main goal of this article consists on the development of a method and its i implementation as an App that allow the detection of the foliar damages from images of coffee leaf that are captured using a smartphone, and identify whether it is rust or leaf miner, and in turn the calculation of its severity degree. The method consists of identifying a leaf from the image and separates it from the background with the use of a segmentation algorithm. In the segmentation process, various types of backgrounds for the image using the HSV and YCbCr color spaces are tested. In the segmentation of foliar damages, the Otsu algorithm and the iterative threshold algorithm, in the YCgCr color space, have been used and compared to k-means. Next, features of the segmented foliar damages are calculated. For the classification, artificial neural network trained with extreme learning machine have been used. The results obtained shows the feasibility and effectiveness of the approach to identify and classify foliar damages, and the automatic calculation of the severity. The results obtained are very promising according to experts. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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Semi-Supervised Online Elastic Extreme Learning Machine for Data Classification

Semi-Supervised Online Elastic Extreme Learning Machine for ...

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International Joint Conference on Neural Networks

作者： Carlos A. S. da Silva Renato A. Krohling Graduate Program in Computer Science PPGI - Federal University of Espirito Santo (UFES) Vitoria ES Brazil Production Engineering Department Federal University of Espirito Santo (UFES) Vitoria ES Brazil

The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently, with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.

关键词： Training Partitioning algorithms Matrix decomposition Neurons Heuristic algorithms Laplace equations Cost function

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Restricted Boltzmann machine to determine the input weights for extreme learning machines

arXiv

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arXiv 2017年

作者： Pacheco, Andre G.C. Krohling, Renato A. da Silva, Carlos A.S. Graduate Program in Computer Science PPGI Federal University of Espírito Santo - UFES Vitória ES Brazil Production Engineering Department Graduate Program in Computer Science PPGI Federal University of Espírito Santo - UFES Vitória ES Brazil

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and do not change them in the whole process. Although the network works well, the random weights in the input layer can make the algorithm less effective and impact on its performance. Therefore, we propose a new approach to determine the input weights and bias for the ELM using the restricted Boltzmann machine (RBM), which we call RBM-ELM. We compare our new approach with a well-known approach to improve the ELM and a state of the art algorithm to select the weights for the ELM. The results show that the RBM-ELM outperforms both methodologies and achieve a better performance than the ELM. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge acquisition

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An Investigation of the Hoeffding Adaptive Tree for the Problem of Network Intrusion Detection

An Investigation of the Hoeffding Adaptive Tree for the Prob...

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International Joint Conference on Neural Networks

作者： Diego Guarnieri Corrêa Fabrício Enembreck Carlos N. Silla Graduate Program in Informatics (PPGI) - Federal University of Technology of Paraná (UTFPR) Cornélio Procópio PR Brazil 86300-000 Graduate Program in Computer Science (PPGIa) - Pontifical Catholic University of Paraná (PUCPR) Curitiba PR Brazil 80215-901

ISBN: (纸本)9781509061839

Intrusion detection in computer networks is a important topic in information security. Due to numerous cases of security breaches that caused economic and social losses in recent years, this topic has been the subject of several studies in order to mitigate problems related to network intrusion and computer attacks. Information security systems have been using different techniques for network intrusion detection. However, with the development of communication mechanisms and consequently with the increase in data traffic, some techniques used for intrusion detection lost their information processing capability. The emergence of new forms of attacks on computer systems also contribute to the depreciation of some of the existing tools. In this scenario, new techniques capable of processing large amounts of information and that perform proactive discovery of new attack vectors are necessary. This paper presents a study on the use of a data stream mining technique known as Hoeffding Adaptive Tree to create a predictive model for network intrusion detection. The experiments performed in this work show the effectiveness of this technique when applied to a database of computer network attacks.

关键词： Intrusion detection Data mining Tools computers Databases Prediction algorithms Adaptation models

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TODIM Based Method to Process Heterogeneous Information

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Procedia computer science 2015年 55卷 318-327页

作者： Rodolfo Lourenzutti Renato A. Krohling Graduate Program in Computer Science PPGI UFES Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitoria CEP 29075-910 Brazil Department of Production Engineering & Graduate Program in Computer Science PPGI UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitoria CEP 29075-910 Esprito Santo ES Brazil

Multicriteria decision making (MCDM) problems are getting increasingly difficult over the years. Nowadays, it is very common to have a problem where heterogeneous types of information must be processed before making a decision. From a wide variety of MCDM methods, just a few of them are able to process mixed types of information at the same time and most of these methods relies on transformations that may cause problems and/or be unjustified. In this paper we bring a modular interpretation of the TODIM (an acronym in Portuguese for Interative Multi-criteria Decision Making) method to handle heterogeneous data types simultaneously, in a systematic way. We argue that the method have a modular capacity which is the novelty of our approach. Using this interpretation, the whole problem with heterogeneous information is broken into modules and processed in a straightforward way. Two examples are used to illustrate the approach showing the effectiveness and practicability.

关键词： Multi-criteria decision making (MCDM) TODIM heterogeneous information interval numbers fuzzy numbers intuitionistic fuzzy numbers.

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Differential Evolution and Nelder-mead for Constrained Non-linear Integer Optimization Problems

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Procedia computer science 2015年 55卷 668-677页

作者： Felipe Luchi Renato A. Krohling Graduate Program in Science Computer PPGI UFES - Federal University of Esṕırito Santo Av. Fernando Ferrari 514 CEP 29075-910 Vitória Esṕırito Santo ES Brazil Departament of Production Engineering & Graduate Program in Computer Science PPGI UFES - Federal University of Esṕırito Santo Av. Fernando Ferrari 514 CEP 29075-910 Vitória Esṕırito Santo ES Brazil

We present a novel approach to solve constrained non-linear integer optimization problems based on Differential Evolution (DE) and Nelder-Mead (NM). DE is a promising technique used in non-differentiable and non-linear problems with continuous variables. It is used to identify promising regions in the search space. NM is a derivative-free technique used in non-linear continuous optimization problems. Since we are concerned with integer problems, then the NM is extended to handle with integer optimization problems. The constraints are treated by the Alpha Constrained method, where constraints values and fitness are compared using a lexicographical order. Since DE is used to continuous optimization and NM needs an initial starting point, we propose a method that use the best individual of DE as starting point to NM. Simulation results show the effectiveness of the proposed method.

关键词： Constrained non-linear integer optimization Differential Evolution Nelder-Mead Alpha Constraint method

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A-TOPSIS – An Approach Based on TOPSIS for Ranking Evolutionary Algorithms

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Procedia computer science 2015年 55卷 308-317页

作者： Renato A. Krohling André G.C. Pacheco Production Engineering Department UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitória CEP: 29060-270 Brazil Graduate Program in Computer Science PPGI UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitória CEP: 29060-270 Brazil

In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation.

关键词： Algorithms comparison ranking TOPSIS evolutionary algorithms

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Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation

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Procedia computer science 2015年 55卷 990-999页

作者： Rafael Hrasko André G.C. Pacheco Renato A. Krohling Graduate Program in Computer Science PPGI UFES - Universidade Federal do Espirito Santo Av. Fernando Ferrari 514 Vitória 29075-910 Brazil Production Engineering Department UFES - Universidade Federal do Espirito Santo Av. Fernando Ferrari 514 Vitória 29075-910 Brazil

Time series prediction appear in many real-world problems, e.g., financial market, signal processing, weather forecasting among others. The underlying models and time series data of those problems are generally complex in a way that reasonable accurate estimation cannot be easily achieved, thus requiring more advanced techniques. Statistical models are the classical approaches for tackling this problem. Many works extended different architectures of Artificial neural networks to work with time series prediction, such as Feedforward, Boltzmann Machines and Deep Belief Network. A Deep Belief Network based on hybridization between Gaussian-Bernoulli Restricted Boltzmann Machine and the Backpropagation algorithm is presented. The hybrid algorithm is tested on three time series databases showing promising results.

关键词： Time Series Prediction Gaussian-Bernoulli Restrict Boltzmann Machine Deep Belief Network

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