Subscribe: Logic Journal of IGPL - Advance Access
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Logic Journal of the IGPL Advance Access

Published: Sat, 23 Sep 2017 00:00:00 GMT

Last Build Date: Sat, 23 Sep 2017 01:49:38 GMT


On the structure theory of Łukasiewicz near semirings


In a previous article by two of the present authors and S. Bonzio, Łukasiewicz near semirings were introduced and it was proven that basic algebras can be represented (precisely, are term equivalent to) as near semirings. In the same work it has been shown that the variety of Łukasiewicz near semirings is congruence regular. In other words, every congruence is uniquely determined by its 0-coset. Thus, it seems natural to wonder whether it could be possible to provide a set-theoretical characterization of these cosets. This article addresses this question and shows that kernels can be neatly described in terms of two simple conditions. As an application, we obtain a concise characterization of ideals in Łukasiewicz semirings. Finally, we close this article with a rather general Cantor–Bernstein type theorem for the variety of involutive idempotent integral near semirings.

Editorial: Special Issue HAIS15-IGPL


The thirteen papers included in this special issue represent a selection of extended contributions presented at the 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015 held in Bilbao, Spain, March 22nd–24th 2015, and organized by the BISITE and the DeustoTech research groups.

An ensemble algorithm for Kohonen self-organizing map with different sizes


Data Clustering aims to discover groups within the data based on similarities, with a minimal, if any, knowledge of their structure. Variations in the results may occur due to many factors, including algorithm parameters, initialization and stopping criteria. The usage of different attributes or even different subsets of data usually lead to different results. Self-organizing maps (SOM) has been widely used for a variety of tasks regarding data analysis, including data visualization and clustering. A machine committee, or ensemble, is a set of neural networks working independently with some system that enable the combination of individual results into a single output, with the aim to achieve a better generalization compared to a unique neural network. This article presents a new ensemble method that uses SOM networks. Cluster validity indexes are used to combine neuron weights from different maps with different sizes. Results are shown from simulations with real and synthetic data, from the UCI Repository and Fundamental Clustering Problems Suite. The proposed method presented promising results, with increased performance compared with conventional single Kohonen map.

Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning


This article presents a hybrid methodology in which a KDD scheme is optimized to build accurate parsimonious models. The methodology tries to find the best model by using genetic algorithms to optimize a KDD scheme formed with the following stages: feature selection, transformation of the skewed input and the output data, parameter tuning and parsimonious model selection. The results obtained demonstrated the optimization of these steps that significantly improved the model generalization capabilities in some UCI databases. Finally, this methodology was applied to create room demand parsimonious models using booking databases from a hotel located in a region of Northern Spain. The results proved that the proposed method created models with higher generalization capacity and lower complexity compared to those obtained with classical KDD process.