Neural Network World; International Journal on Neural and Mass - Parallel Computing and Information Systems

An Electronic Performance Support System Based On a Hybrid Content-Collaborative Recommender System

An Electronic Performance Support System (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project1 aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering a...

An Optimized Evolutionary Conditional Independence Bayesian Classifier Induction Process

Bayesian Networks (BNs) are graphical models which represent multivariate joint probability distributions which have been used successfully in several studies in many application areas. BN learning algorithms can be remarkably effective in many problems. The search space for a BN induction, however, has an exponential dimension. Therefore, finding the BN structure that better represents the dependencies among the variables is known to be a NP problem. This work proposes and discusses a hybrid...

Artificial Immune System with Art Memory Hibridization

The present work proposes the architecture Clonart (Clonal Adaptive Resonance Theory), a Hybrid Model that employs techniques like intelligent operators, clonal selection principle, local search, memory antibodies and ART clusterization, in order to increase the performance of the algorithm. The approach uses a mechanism similar to the ART 1 network for storing a population of memory antibodies that will be responsible for the acquired knowledge of the algorithm. This characteristic allows th...

Forecasting Time Series Data Using Hybrid Grey Relational Artificial Neural Network and Auto Regressive Integrated Moving Average Model

In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time series data. The pro...

Many-Objective Training of a Multi-Layer Perceptron

In this paper, a many-objective training scheme for a multi-layer feed-forward neural network is studied. In this scheme, each training data set, or the average over sub-sets of the training data, provides a single objective. A recently proposed group of evolutionary many-objective optimization algorithms based on the NSGA-II algorithm have been examined with respect to the handling of such problem cases. A modified NSGA-II algorithm, using the norm of an individual as a secondary ranking ass...

A Hyper-Heuristic for Adaptive Scheduling in Computational Grids

In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in Computational Grids. An efficient scheduling of jobs to Grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of Grid environment, therefore the planning of jobs to resources sh...

Design of Directed Acyclic Graph Multiclass Structures

One of the approaches adopted to generate multiclass classifiers from binary predictors is to decompose the multiclass problem into multiple binary subproblems. Among the existing decomposition approaches, one may cite the use of Directed Acyclic Graphs (DAG) to combine pairwise classifiers. This work presents a study on the influence of the DAG structure in the performance obtained in multiclass problems when Support Vector Machines are used in the induction of the binary predictors.

On the Road to Genetic Boolean Matrix Factorization

Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background and initial version...

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