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

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

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

Beta hebbian learning: definition and analysis of a new family of learning rules for exploratory projection pursuit

[EN] This thesis comprises an investigation into the derivation of learning rules in artificial neural networks from probabilistic criteria. •Beta Hebbian Learning (BHL). First of all, it is derived a new family of learning rules which are based on maximising the likelihood of the residual from a negative feedback network when such residual is deemed to come from the Beta Distribution, obtaining an algorithm called Beta Hebbian Learning, which outperforms current neural algorithms in Exploratory Projection Pursuit. • Beta-Scale Invariant Map (Beta-SIM). Secondly, Beta Hebbian Learning is applied to a well-known Topology Preserving Map algorithm called Scale Invariant Map (SIM) to design a new of its version called Beta-Scale Invariant Map (Beta-SIM). It is developed to facilitate the clustering and visualization of the internal structure of high dimensional complex datasets effectively and efficiently, specially those characterized by having internal radial distribution. The Beta-SIM behaviour is thoroughly analysed comparing its results, in terms performance quality measures with other well-known topology preserving models. • Weighted Voting Superposition Beta-Scale Invariant Map (WeVoS-Beta-SIM). Finally, the use of ensembles such as the Weighted Voting Superposition (WeVoS) is tested over the previous novel Beta-SIM algorithm, in order to improve its stability and to generate accurate topology maps when using complex datasets. Therefore, the WeVoS-Beta-Scale Invariant Map (WeVoS-Beta-SIM), is presented, analysed and compared with other well-known topology preserving models. All algorithms have been successfully tested using different artificial datasets to corroborate their properties and also with high-complex real datasets.[ES] Esta tesis abarca la investigación sobre la derivación de reglas de aprendizaje en redes neuronales artificiales a partir de criterios probabilísticos. • Beta Hebbian Learning (BHL). En primer lugar, se deriva una nueva familia de reglas de aprendizaje basadas en maximizar la probabilidad del residuo de una red con retroalimentación negativa cuando se considera que dicho residuo proviene de la Distribución Beta, obteniendo un algoritmo llamado Beta Hebbian Learning, que mejora a algoritmos neuronales actuales de búsqueda de proyecciones exploratorias. • Beta-Scale Invariant Map (Beta-SIM). En Segundo lugar, Beta Hebbian Learning se aplica a un conocido algoritmo de Mapa de Preservación de la Topología llamado Scale Invariant Map (SIM) para diseñar una nueva versión llamada Beta-Scale Invariant Map (Beta-SIM). Este nuevo algoritmo ha sido desarrollado para facilitar el agrupamiento y visualización de la estructura interna de conjuntos de datos complejos de alta dimensionalidad de manera eficaz y eficiente, especialmente aquellos caracterizados por tener una distribución radial interna. El comportamiento de Beta-SIM es analizado en profundidad comparando sus resultados, en términos de medidas de calidad de rendimiento con otros modelos bien conocidos de preservación de topología. • Weighted Voting Superposition Beta-Scale Invariant Map (WeVoS-Beta-SIM). Finalmente, el uso de ensembles como el Weighted Voting Superposition (WeVoS) sobre el algoritmo Beta-SIM es probado, con objeto de mejorar su estabilidad y generar mapas topológicos precisos cuando se utilizan conjuntos de datos complejos. Por lo tanto, se presenta, analiza y compara el WeVoS-Beta-Scale Invariant Map (WeVoS-Beta-SIM) con otros modelos bien conocidos de preservación de topología. Todos los algoritmos han sido probados con éxito sobre conjuntos de datos artificiales para corroborar sus propiedades, así como con conjuntos de datos reales de gran complejidad

A novel ensemble Beta-scale invariant map algorithm

[Abstract]: This research presents a novel topology preserving map (TPM) called Weighted Voting Supervision -Beta-Scale Invariant Map (WeVoS-Beta-SIM), based on the application of the Weighted Voting Supervision (WeVoS) meta-algorithm to a novel family of learning rules called Beta-Scale Invariant Map (Beta-SIM). The aim of the novel TPM presented is to improve the original models (SIM and Beta-SIM) in terms of stability and topology preservation and at the same time to preserve their original features, especially in the case of radial datasets, where they all are designed to perform their best. These scale invariant TPM have been proved with very satisfactory results in previous researches. This is done by generating accurate topology maps in an effectively and efficiently way. WeVoS meta-algorithm is based on the training of an ensemble of networks and the combination of them to obtain a single one that includes the best features of each one of the networks in the ensemble. WeVoS-Beta-SIM is thoroughly analyzed and successfully demonstrated in this study over 14 diverse real benchmark datasets with diverse number of samples and features, using three different well-known quality measures. In order to present a complete study of its capabilities, results are compared with other topology preserving models such as Self Organizing Maps, Scale Invariant Map, Maximum Likelihood Hebbian Learning-SIM, Visualization Induced SOM, Growing Neural Gas and Beta- Scale Invariant Map. The results obtained confirm that the novel algorithm improves the quality of the single Beta-SIM algorithm in terms of topology preservation and stability without losing performance (where this algorithm has proved to overcome other well-known algorithms). This improvement is more remarkable when complexity of the datasets increases, in terms of number of features and samples and especially in the case of radial datasets improving the Topographic Error

A topology-preserving system for environmental models forecasting

This inter-disciplinary study presents a novel mathematical simulation model based on an algorithm for the summarization of self-organizing maps ensembles applied under the case-based reasoning (CBR) methodology to perform forecasting tasks. This methodology represents a knowledge-extraction frame, where past information is used to generate new solutions to new problems. The novel summarization algorithm based on topology-preserving models organizes the stored information simplifying the retrieval of the most useful information from the case base. This algorithm is used to organize the case base and to improve the speed and efficiency of the retrieval phase of the CBR cycle within the explained predicting system. The developed mathematical system was applied to a real case of study: a forest fire forecasting data set. Forest fires represent an environmental risk that should be predicted in order to avoid further damages. This novel system was able to predict the future situation of geographic areas after a forest fire had been originated

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

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

A forecasting solution to the oil spill problem based on a hybrid intelligent system

Oil spills represent one of the most destructive environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be critical in reducing environmental risks. The system presented here uses the Case-Based Reasoning (CBR) methodology to forecast the presence or absence of oil slicks in certain open sea areas after an oil spill. CBR is a computational methodology designed to generate solutions to certain problems by analysing previous solutions given to previously solved problems. The proposed CBR system includes a novel network for data classification and retrieval. This type of network, which is constructed by using an algorithm to summarize the results of an ensemble of Self-Organizing Maps, is explained and analysed in the present study. The Weighted Voting Superposition (WeVoS) algorithm mainly aims to achieve the best topographically ordered representation of a dataset in the map. This study shows how the proposed system, called WeVoS-CBR, uses information such as salinity, temperature, pressure, number and area of the slicks, obtained from various satellites to accurately predict the presence of oil slicks in the north-west of the Galician coast, using historical data