María S. Pérez

GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Design and implementation of a data mining grid-aware architecture

Current business processes often use data from several sources. Data is characterized to be heterogeneous, incomplete and usually involves a huge amount of records. This implies that data must be transformed in a set of patterns, rules or some kind of formalism, which helps to understand the underlying information. The participation of several organizations in this process makes the assimilation of data more difficult. Data mining is a widely used approach for the transformation of data to useful patterns, aiding the comprehensive knowledge of the concrete domain information. Nevertheless, traditional data mining techniques find difficulties in their application on current scenarios, due to the complexity previously mentioned. Data Mining Grid tries to fix these problems, allowing data mining process to be deployed in a grid environment, in which data and services resources are geographically distributed, belong to several virtual organizations and the security can be flexibly solved. We propose both a novel architecture for Data Mining Grid, named DMGA, and the implementation of this architecture, named WekaG.

Bayesian network multi-classifiers for protein secondary structure prediction

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.

Adapting the weka data mining toolkit to a grid based environment

Data Mining is playing a key role in most enterprises, which have to analyse great amounts of data in order to achieve higher profits. Nevertheless, due to the large datasets involved in this process, the data mining field must face some technological challenges. Grid Computing takes advantage of the low-load periods of all the computers connected to a network, making possible resource and data sharing. Providing Grid services constitute a flexible manner of tackling the data mining needs. This paper shows the adaptation of Weka, a widely used Data Mining tool, to a grid infrastructure.

An agents-based cooperative awareness model to cover load balancing delivery in grid environments

This paper presents an extension of the AMBLE model, an awareness model which manage load balancing by means of a multi-agent based architecture, with the aim to establish a cooperative load balancing model for collaborative grid environments. This model, named C-AMBLE (Cooperative Awareness Model for Balancing the Load in grid Environments) applies some theoretical principles of multi-agents systems, awareness models, and third party models, to promote an efficient autonomous cooperative task delivery in grid environments. This cooperative task management, implemented using web services, has been tested in a real and heterogeneous grid infrastructure with very successful results. This paper presents some of these outcomes while emphasizing on the performance speedup of the system using this model.

Managing dynamic virtual organizations to get effective cooperation in collaborative grid environments

This paper presents how to manage Virtual Organizations to enable efficient collaboration and/or cooperation as a result of a flexible and parametrical model. The CAM (Collaborative/Cooperative Awareness Management) model promotes collaboration around resources-sharing infrastructures, endorsing interaction by means of a set of rules. This model focuses on responding to specific demanding circumstances at a given moment, while optimizes resources communication and behavioural agility to get a common goal: the establishment of collaborative dynamic virtual organizations. This paper also describes how CAM works in some specific examples and scenarios, and how the CAM Rules-Based Management Application (based on Web Services and named WS-CAM) has been designed and validated to encourage resources to be involved in collaborative performances, tackling efficiently demanding situations without hindering the own purposes of each of these resources.

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

Hybrid metaheuristics have received considerable interest in recent years. A wide variety of hybrid approaches have been proposed in the literature. In this paper a new hybrid approach, named GA-EDA, is presented. This new hybrid algorithm is based on genetic and estimation of distribution algorithms. The original objective is to benefit from both approaches and attempt to achieve improved results in exploring the search space. In order to perform an evaluation of this new approach, a selection of synthetic optimization problems have been proposed, together with some real-world cases. Experimental results show the competitiveness of our new approach.

Improvement of naive Bayes collaborative filtering using interval estimation

Recommender systems emerged to help users choose among the large amount of options that ecommerce sites offer. Collaborative filtering is one of the most successful recommender techniques. Here we propose an approach to collaborative filtering based on the simple Bayesian classifier. We propose a method of increasing the efficiency of naive Bayes by applying a new semi naive Bayes approach based on interval estimation. To evaluate our algorithm we use a database of Microsoft anonymous Web data from the UCl repository. Our empirical results show that our proposed Interval based naive Bayes approach outperforms typical naive Bayes.

A new multiagent based architecture for high performance I/O in clusters

This paper presents MAPFS, a MultiAgent Parallel File System for clusters. MAPFS has been conceived as a client able to interact with different existing traditional or distributed servers and provide them the parallel I/O characteristics. Therefore, MAPFS allows take advantage of the use of the parallelism in clusters of workstations. MAPFS defines a multiagent architecture that defines the storage group concept, as a set of servers that form a group to support the parallel I/O for MAPFS. MAPFS offers a file system interface that include traditional operations, advanced and collective operations, and fault tolerance and caching operations.

A rule based resources management for collaborative grid environments

Something that is still missing, but strongly needed, in collaborative grid environments is a stable, flexible and dynamic resource management. This management should optimise collaboration and cooperation among several resources keeping resources constraints, preconditions and rules. This paper presents how to achieve these objectives, by means of a Collaborative Awareness Management (CAM) model. CAM optimises resources collaboration, promotes resources cooperation and responds to the specific demanded circumstances. This paper also describes how this model works in some specific examples and scenarios, emphasising on how the WS-CAM Rules-Based Management Application has been designed, implemented, and validated to accomplish these purposes.