Journal of Intelligent & Fuzzy Systems - Volume 32, issue 2

Authors: Siemiński, Andrzej | Kopel, Marek

Article Type: Research Article

Abstract: The paper presents a study on the efficiency of Ant Colony Communities (ACC) used to solve the Travelling Salesman Problem. The ACC is an approach to parallelize the Ant Colony Optimization algorithm (ACO). An ACC is made up of a Community Server that coordinates the work of a set ant colony clients. Each client implements a classical ACO algorithm. The individual colonies process cargos of data obtained from the server and send them back the as partial results. The paper presents a general description of the ACC concept and describes in details two ways of implementing it. The first one …uses an inhomogeneous environment of traditional computers working in an asynchronous mode. The second one uses the homogenous Hadoop environment and the processing is done in a synchronized mode. The performance of the Communities is estimated by low level measures: their power and scalability. The high level measure deals with the length of obtained routes. The paper presents also the taxonomy of parallel implementations of the Ant Colony Optimization. Show more

Keywords: Ant Colony Optimization, Travelling Salesman Problem, ACO parallel implementations, sockets, Hadoop, MapReduce, scalability, computational power

DOI: 10.3233/JIFS-169135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1377-1388, 2017

Authors: Tian, Feng | Zhang, Rong | Lewandowski, Jacek | Chao, Kuo-Ming | Li, Longzhuang | Dong, Bo

Article Type: Research Article

Abstract: Consolidation of services is one of the key problems in cloud data centers. It consists of two separate but related issues: Virtual machine (VM) placement and VM migration problems. In this paper, a VM consolidation scheme is proposed that turns the virtual machine consolidation (VMC) problem into a vector packing optimization problem based on deadlock-free migration (DFM) to minimize the energy consumptions. To solve this NP-hard and computationally infeasible for large data centers problem, a novel algorithm named Chicken Swarm Optimization based on deadlock-free migration (DFM-CSO) algorithm is proposed. The DFM-CSO algorithm is characterized by the ‘one-step look-ahead with n-VMs …migration in parallel (OSLA-NVMIP)’ method, which carries out the VM migration validation and the rearrangement of target physical host, as well as records the migration order for each solution placement, so that VM transfer can be completed according to the migration sequence. The experimental results, for both real and synthetic datasets, show that the proposed algorithm with higher convergence rate is favourable in comparison with the other deadlock-free migration algorithms. Show more

Keywords: VM consolidation, VM placement, deadlock-free migration, Chicken Swarm Optimization

DOI: 10.3233/JIFS-169136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1389-1400, 2017

Authors: Czarnowski, Ireneusz | Jędrzejowicz, Piotr

Article Type: Research Article

Abstract: Data reduction can increase generalization abilities of the learning model and shorten learning time. It can be particularly helpful in analyzing big data sets. This paper focuses on the machine learning from examples with data reduction. In the paper data reduction is carried out by selection of relevant instances, called prototypes. The discussed approach bases on the assumption that the selection of prototypes is carried-out by a team of agents and that the prototype instances are selected from clusters of instances under the constraint that from each cluster a single prototype is obtained. For cluster initialization the kernel-based fuzzy clustering …algorithm is used. Main feature of the proposed approach is integrating data reduction with the stacking technique. Stacked generalization assures diversification among prototypes, and hence, base classifiers. To validate the proposed approach we have carried-out computational experiment. We have also evaluated experimentally the influence of the clustering method and the number of stacking folds used, on the classification accuracy. Show more

Keywords: Learning from big data, data reduction, stacked generalization, kernel-based clustering

DOI: 10.3233/JIFS-169137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1401-1411, 2017

Authors: Lin, Szu-Yin | Chiu, Yao-Ching | Lewandowski, Jacek | Chao, Kuo-Ming

Article Type: Research Article

Abstract: The traditional data analysis and prediction method assumes that data distribution is normal and will not change. Therefore, it can predict unlabeled data by analyzing the static and historical data. However, in today’s big-data environment, which is changing frequently, the traditional approaches can no longer be effective, as they cannot handle concept drift problems in a Dynamic Data Driven Application System (DDDAS). This study proposes a parallel detection and prediction method for concept drift problems in DDDAS. The proposed method can detect dynamic and changing data, and then feedback to the prediction model to revise for better subsequent predictions. Furthermore, …this method computes a global prediction result by aggregating local predictions in the resource bounded environment. Therefore, the prediction accuracy increases, and the computation time decreases. In the simulation, the Map-Reduce technology is used for parallel processing. The simulation results show that the prediction accuracy is raised by 14%, and the execution time is improved by almost 45%. Show more

Keywords: Dynamic data-driven application system, concept drift, Map-Reduce

DOI: 10.3233/JIFS-169138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1413-1426, 2017

Authors: Ksieniewicz, Paweł | Graña, Manuel | Woźniak, Michał

Article Type: Research Article

Abstract: Recently, the representation learning is the fucus of intense research of machine learning community. The underlying idea is that the key for successful discrimination of difficult datasets is a good feature extraction. A transformation of the data space into another space where classification is easy. This work proposes a novel transformation into feature space that follows a photographic intuition: that we can build from pairs of features in original space some kind of photographic plate where the sample data are projected to create a picture of the data distribution in the feature subspace defined by the feature pair. These …photographic plates may be used as individuals of a classifier ensemble. The approach allows a natural definition of a confidence weight affecting each individual classifier out for the construction of a combination rule used by the ensemble. Hence the name Paired Feature Multilayer Ensemble (PFME ). The approach is naturally naive parallel, insensitive to sample size, robust to dimension increase, and allows a regularization in feature space which is independent from original input space. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark datasets. Show more

Keywords: Machine learning, representation learning, classifier ensemble, hyperspectral image

DOI: 10.3233/JIFS-169139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1427-1436, 2017

Authors: Nguyen, Tuong Tri | Hwang, Dosam | Jung, Jason J.

Article Type: Research Article

Abstract: The imbalanced data problem occurs when the number of representative instances for classes of interest is much lower than for other classes. The influence of imbalanced data on classification performance has been discussed in some previous research as a challenge to be studied. In this paper, we propose a method to solve the imbalanced data problem by focusing on preprocessing, including: i) sampling techniques (i.e., under-sampling, over-sampling, and hybrid-sampling) and ii) the instance weighting method to increase the number of features in minority classes and to reduce comprehensive coverage in majority classes. The experimental results show that the noisy data …is reduced, making a smaller sized dataset, and training time decreases significantly. Moreover, distinct properties of each class are examined effectively. Refined data is used as input for Naive Bayes and support vector machine classifiers for the targets of the training process. The proposed methods are evaluated based on the number of non-geotagged resources that are labeled correctly with their geo-locations. In comparison with previous research, the proposed method achieves accuracy of 84%, whereas previous results were 75%. Show more

Keywords: Imbalanced datasets, geotags resources, sampling method, instance weighting, location prediction

DOI: 10.3233/JIFS-169140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1437-1448, 2017

Authors: García-Saiz, Diego | Zorrilla, Marta

Article Type: Research Article

Abstract: The task of selecting the most suitable classification algorithm for each data set under analysis is still today a unsolved research problem. This paper therefore proposes a meta-learning based framework that helps both, practitioners and non-experts data mining users to make informed decisions about the goodness and suitability of each available technique for their data set at hand. In short, the framework is supported by an experimental database that is fed with the meta-features extracted from training data sets and the performance obtained by a set of classifiers applied over them, with the aim of building an algorithm recommender using …regressors. This will allow the end-user to know, for a new unseen data set, the predicted accuracy of this set of algorithms ranked by this value. The experimentation performed and discussed in this paper is addressed to evaluate which meta-features are more significant and useful for characterising data sets with the end goal of building algorithm recommenders and to test the feasibility of these recommenders. The study is carried out on data sets from the educational arena, in particular, targeted to predict students’ performance in e-learning courses. Show more

Keywords: Meta-learning, regression, student performance, educational data mining

DOI: 10.3233/JIFS-169141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1449-1459, 2017

Authors: Król, Dariusz | Nowakowski, Filip

Article Type: Research Article

Abstract: This paper assesses the possibility of using a popular middleware platform based on a multiple agent paradigm in full compliance with Real Time Specifications for Java. Two reference scenarios are discussed: one testing thread-to-thread activation (creating and releasing threads), the other featuring agent-to-agent execution for road traffic simulation (creating and releasing agents). These preemptive tasks must be scheduled with minimum delay, therefore, timing correctness as thread-to-thread activation latency and agent-to-agent execution latency is a critical performance index. Given this requirement, the study presented describes an empirical investigation of timing and capacity impact on two platforms: Solaris and Windows …in both RT and non-RT versions. The experiments showed the impact on performance of platform type and of capacity with the non-RT approach in particular yielding less accuracy but better stability in agent execution. Suggestions as to how this real-time multi-agent approach might be made more effective are included in the paper. Show more

Keywords: Multi-agent simulation, real-time system, middleware platform, JADE, FIPA-compliant system, vehicle traffic system, performance evaluation, timing correctness

DOI: 10.3233/JIFS-169142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1461-1473, 2017

Authors: Sobeslav, Vladimir | Balik, Ladislav | Hornig, Ondrej | Horalek, Josef | Krejcar, Ondrej

Article Type: Research Article

Abstract: This article presents a security system proposal, providing a low-level endpoint security and network activity monitoring. Its focus is to provide a necessary information for local administrators, who does not necessarily have the knowledge of networking infrastructure or access to it, according to the security policies of a parent organization. The proposed system is designed for academic research environments, where it serves as a tool for an extended security in protection of sensitive data used in research and development against the local and remote threads. The developed system was extended to contain central security point which acts as a server …with IDS/IPS capability and enrich the whole functionality of distributed firewalling system. Show more

Keywords: Firewall, endpoint, local security, packet inspection, iptables, advfirewall, java firewall, research data security, open source

DOI: 10.3233/JIFS-169143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1475-1484, 2017

Authors: Camacho, Azahara | Merayo, Mercedes G. | Núñez, Manuel

Article Type: Research Article

Abstract: Healthcare is one of the most important concerns of society, being extremely relevant the accuracy and quality of their services. Basically, eHealth can be considered as the area where electronic processes and communications are used to improve the quality of medical assistance. Despite the relative maturity of the field, recalls and problems related to medical devices, applications and services are still very frequent. Therefore, it is necessary to improve and expand current research to provide advances that can be transmitted to the society. As an initial step, it is essential to have a good understanding of the current state-of-the-art of …eHealth. The main goal of this paper is to identify the most relevant and recent work on eHealth but, due to the immensity of the field and the scope of the journal, we will concentrate on the use of two specific technologies: databases and collective intelligence. In addition to review the main concepts related to eHealth and the most influent academic papers, we will describe projects which are essential for the correct performance of many healthcare services. As a result of our study, we reached some interesting conclusions that might be useful for future projects and we encourage to apply them in order to avoid some of the problems that we have found in existing projects. Show more

Keywords: eHealth, databases, collective intelligence, security

DOI: 10.3233/JIFS-169144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 2, pp. 1485-1496, 2017