Yenny Villuendas-Rey

Instance-based ontology matching for e-learning material using an associative pattern classifier

The present work describes a new model of pattern classification and its application to align instances from different ontologies, which are in turn related to e-learning educative content in a Knowledge Society context. In general, ontologies are the fundamental tool inherent to Semantic Web. In particular, the problem of ontology matching is modeled in this paper as a binary pattern classification problem. The original model presented here was validated through experiments, which were done on data taken from the OAEI (Ontology Alignment Evaluation Initiative) 2014 campaign, presented in the OWL (Web Ontology Language) format, as well as on data taken from two international repositories, ADRIADNE and MERLOT, in LOM (Learning Objects Metadata) format. The results obtained show a high precision measurement when compared against some of the best methods present in the state of the art. A new model for ontology matching over educative content repositories is introduced.The model was validated on the OAEI 2014 campaign, ADRIADNE and MERLOT.The homogeneity of resources for e-learning is improved.The results obtained show a high precision measurement.

The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data

Abstract In this paper the Naive Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed.

Automatic feature weighting for improving financial Decision Support Systems

Abstract We propose a novel methodology for improving financial Decision Support Systems (DSS) through automatic feature weighting. Using this methodology, we show that automatic feature weighting leads to a significant improvement in the performance of decision-making algorithms over financial data, which are the key of financial DSS. The statistical analysis carried out shows that metaheuristic algorithms are good for automatic feature weighting, and that Differential Evolution (DE) offers a good trade-off between decision-making performance and computational cost. We believe these results contribute to the development of novel financial DSS.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications

The current paper contains the theoretical foundation for the off-the-mainstream model known as Alpha-Beta associative memories (

Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

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Instance Selection in the Performance of Gamma Associative Classifier

αβ model). This is an unconventional computation model designed to operate as an associative memory, whose main application is the solution of pattern recognition tasks, particularly for pattern recall and pattern classification. Although this model was devised, proposed and created in 2002, it is worth noting that its theoretical support remains unpublished to this day. This is despite the fact that more than a hundred scientific articles have been published with applications, improvements, and new models derived from the

The Potential Use of Bioinspired Algorithms Applied in the Segmentation of Mammograms.

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