WeVoS-ViSOM: an ensemble summarization algorithm for enhanced data visualization

This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity—based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualizationThis research has been partially supported through projects CIT-020000-2008-2 and CIT-020000-2009-12 of the Spanish Ministry of Education and Innovation and project BUO06A08 of the Junta of Castilla and Leon. The authors would also like to thank the manufacturer of components for vehicle interiors, Grupo Antolin Ingenieria, S.A. within the framework of the MAGNO2008-1028 CENIT project, funded by the Spanish Ministry of Science and Innovatio

Herramienta web para la auto-evaluación en ejercicios de programación

Póster presentado en: VIII Jornadas de Innovación Docente de la UBU, Burgos, 5 de abril de 2016, organizadas por el Instituto de Formación e Innovación Educativa-IFIE de la Universidad de Burgo

Mutating network scans for the assessment of supervised classifier ensembles

As it is well known, some Intrusion Detection Systems (IDSs) suffer from high rates of false positives and negatives. A mutation technique is proposed in this study to test and evaluate the performance of a full range of classifier ensembles for Network Intrusion Detection when trying to recognize new attacks. The novel technique applies mutant operators that randomly modify the features of the captured network packets to generate situations that could not otherwise be provided to IDSs while learning. A comprehensive comparison of supervised classifiers and their ensembles is performed to assess their generalization capability. It is based on the idea of confronting brand new network attacks obtained by means of the mutation technique. Finally, an example application of the proposed testing model is specially applied to the identification of network scans and related mutationsSpanish Ministry of Science and Innovation (TIN2010-21272-C02-01 and CIT-020000-2009-12) (both funded by the European Regional Development Fund). The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S. A., within the framework of the MAGNO2008 - 1028.- CENIT. Project also funded by the MICINN, the Spanish Ministry of Science and Innovation (PID 560300-2009-11) and the Regional Government of Castile-Leon (CCTT/10/BU/0002). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. (CZ.1.05/1.1.00/02.0070) supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech Republic.This is a pre-copyedited, author-produced PDF of an article accepted for publication in Logic Journal of the IGPL following peer review. The version of record: Javier Sedano, Silvia González, Álvaro Herrero, Bruno Baruque, and Emilio Corchado, Mutating network scans for the assessment of supervised classifier ensembles, Logic Jnl IGPL, first published online September 3, 2012, doi:10.1093/jigpal/jzs037 is available online at: http://jigpal.oxfordjournals.org/content/early/2012/09/03/jigpal.jzs03

WeVoS scale invariant map

A novel method for improving the training of some topology preserving algorithms characterized by its scale invariant mapping is presented and analyzed in this study. It is called Weighted Voting Superposition (WeVoS), and in this research is applied to the Scale Invariant Feature Map (SIM) and the Maximum Likelihood Hebbian Learning Scale Invariant Map (Max-SIM) providing two new versions, the WeVoS–SIM and the WeVoS–Max-SIM. The method is based on the training of an ensemble of networks and the combination of them to obtain a single one, including the best features of each one of the networks in the ensemble. To accomplish this combination, a weighted voting process takes place between the units of the maps in the ensemble in order to determine the characteristics of the units of the resulting map. To provide a complete comparative study of these new models, they are compared with their original models, the SIM and Max-SIM and also to probably the best known topology preserving model: the Self-Organizing Map. The models are tested under the frame of two ad hoc artificial data sets and a real-world one, characterized for having an internal radial distribution. Four different quality measures have been applied for each model in order to present a complete study of their capabilities. The results obtained confirm that the novel models presented in this study based on the application of WeVoS can outperform the classic models in terms of organization of the presented information

Fusion Methods for Unsupervised Learning Ensembles

The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topologypreserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems

A Bio-inspired Fusion Method for Data Visualization

This research presents a novel bio-inspired fusion algorithm based on the application of a topology preserving map called Visualization Induced SOM (ViSOM) under the umbrella of an ensemble summarization algorithm, the Weighted Voting Superposition (WeVoS). The presented model aims to obtain more accurate and robust maps, also increasing the models stability by means of the use of an ensemble training schema and a posterior fusion algorithm, been those very suitable for visualization and also classification purposes. This model may be applied alone or under the frame of hybrid intelligent systems, when used for instance in the recovery phase of a case based reasoning system. For the sake of completeness, the comparison of the performance with other topology preserving maps and previous fusion algorithms with several public data set obtained from the UCI repository are also included

Fusion of Visualization Induced SOM

In this study ensemble techniques have been applied in the frame of topology preserving mappings with visualization purposes. A novel extension of the ViSOM (Visualization Induced SOM) is obtained by the use of the ensemble meta-algorithm and a later fusion process. This main fusion algorithm has two different variants, considering two different criteria for the similarity of nodes. These criteria are Euclidean distance and similarity on Voronoi polygons. The goal of this upgrade is to improve the quality and robustness of the single model. Some experiments performed over different datasets applying the two variants of the fusion and other simpler models are included for comparison purposes

Maximum Likelihood Topology Preserving Ensembles

Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generations of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of a topology preserving map which can be used for scale invariant classification, taking into account the fact that it models the residual after feedback with a family of distributions and finds filters which make the residuals most likely under this model. This model is applied to artificial data sets and compared with a similar version based on the Self Organising Map (SOM)

Quality of Adaptation of Fusion ViSOM

This work presents a research on the performance capabilities of an extension of the ViSOM (Visualization Induced SOM) algorithm by the use of the ensemble meta-algorithm and a later fusion process. This main fusion process has two different variants, considering two different criteria for the similarity of nodes. These criteria are Euclidean distance and similarity on Voronoi polygons. The capabilities, strengths and weakness of the different variants of the model are discussed and compared more deeply in the present work. The details of several experiments performed over different datasets applying the variants of the fusion to the ViSOM algorithm along with same variants of fusion with the SOM are included for this purpose

Solving the Oil Spill Problem Using a Combination of CBR and a Summarization of SOM Ensembles

In this paper, a forecasting system is presented. It predicts the presence of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. CBR systems are designed to generate solutions to a certain problem by analysing historical data where previous solutions are stored. The system explained includes a novel network for data classification and retrieval. Such network works as a summarization algorithm for the results of an ensemble of Self-Organizing Maps. This algorithm, called Weighted Voting Superposition (WeVoS), is aimed to achieve the lowest topographic error in the map. The WeVoS-CBR system has been able to precisely predict the presence of oil slicks in the open sea areas of the north west of the Galician coast