Rodrigo Salas

Self-Adaptive Fuzzy QoS-Driven Web Service Discovery

Due to the high proliferation of web services, selecting the best services from functional equivalent service providers have become a real challenge, where the quality of the services plays a crucial role. But quality is uncertain, therefore, several researchers have applied Fuzzy logic to address the imprecision of the quality of service (QoS) constraints. Furthermore, the service market is highly dynamic and competitive, where web services are constantly entering and exiting this market, and they are continually improving themselves due to the competition. Current fuzzy-based techniques are expert and/or consensus-based, and therefore too fragile, expensive, non-scalable and non-self-adaptive. In this paper we introduce a new methodology to support requesters in selecting Web services by automatically connecting imprecisely defined QoS constraints with overly precise service QoS offerings over the time. We address the dynamism of the market by using each time a modified fuzzy c-means module that allows providers to automatically organize themselves around the QoS levels. The advantage of our approach is that consumers can specify their QoS constraints without really knowing what are the current best quality ranges. We illustrate our approach with a case of study.

Fusion of self organizing maps

An important issue in data-mining is to find effective and optimal forms to learn and preserve the topological relations of highly dimensional input spaces and project the data to lower dimensions for visualization purposes. In this paper we propose a novel ensemble method to combine a finite number of Self Organizing Maps, we called this model Fusion-SOM. In the fusion process the nodes with similar Voronoi polygons are merged in one fused node and the neighborhood relation is given by links that measures the similarity between these fused nodes. The aim of combining the SOM is to improve the quality and robustness of the topological representation of the single model. Computational experiments show that the Fusion-SOM model effectively preserves the topology of the input space and improves the representation of the single SOM. We report the performance results using synthetic and real datasets, the latter obtained from a benchmark site.

A new aspect for the optimization of fuzzy if-then rules

Evolutionary optimization of fuzzy if-then rules for approximation is an area of research that has received much attention in the last years. The present paper adds a new possibility by proposing a method for data-driven reshaping or designing the uncertainty transitions of piecewise linear fuzzy sets representing the linguistic terms of the fuzzy rules.

Self-Organizing Neuro-Fuzzy Inference System

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogerss ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a users performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Robust growing hierarchical self organizing map

The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural network model that adapts its architecture during its unsupervised training process to represents the hierarchical relation of the data. However, the dynamical algorithm of the GHSOM is sensitive to the presence of noise and outliers, and the model will no longer preserve the topology of the data space as we will show in this paper. The outliers introduce an influence to the GHSOM model during the training process by locating prototypes far from the majority of data and generating maps for few samples data. Therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the GHSOM algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust GHSOM (RGHSOM). We will illustrate our technique on synthetic and real data sets.

Robust self-organizing maps

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data.

Zernike's Feature Descriptors for Iris Recognition with SVM

Valuable information of the iris is intrinsically located in its natural texture, therefore preserve and extract the most relevant features for biometric recognition is of paramount importance. The iris pattern is subject to translation, scaling and rotation, consequently the variations produced by these artifacts must be minimized. The main contribution of this work consists on performing a comparison between the descriptive power of the Zernike and pseudo Zernike polynomials for the identification of iris images using a Support Vector Machine (SVM) as a classifier. Experiments with the iris data set obtained from the Bath University repository show that our proposal yields high levels of accuracy.

Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging

Image segmentation is a fundamental technique in medical applications. For example, the extraction of biometrical parameter of tumors is of paramount importance both for clinical practice and for clinical studies that evaluate new brain tumor therapies. Tumor segmentation from brain Magnetic Resonance Images (MRI) is a difficult task due to strong signal heterogeneities and weak contrast at the boundary delimitation. In this work we propose a new framework to segment the Glioblastoma Multiforme (GBM) from brain MRI. The proposed algorithm was constructed based on two well known techniques: Region Growing and Fuzzy C-Means. Furthermore, it considers the intricate nature of the GBM in MRI and incorporates a fuzzy formulation of Region Growing with an automatic initialization of the seed points. We report the performance results of our segmentation framework on brain MRI obtained from patients of the chilean Carlos Van Buren Hospital and we compare the results with Region Growing and the classic Fuzzy C-Means approaches.

Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

This work presents a new method for feature extraction of iris images to improve the identification process. The valuable information of the iris is intrinsically located in its natural texture, and preserving and extracting the most relevant features is of paramount importance. The technique consists in several steps from adquisition up to the person identification. Our contribution consists in a multimodal algorithm where a fragmentation of the normalized iris image is performed and, afterwards, regional statistical descriptors with Self-Organizing-Maps are extracted. By means of a biometric fusion of the resulting descriptors, the features of the iris are compared and classified. The results with the iris data set obtained from the Bath University repository show an excellent accuracy reaching up to 99.867%.

Robust expectation maximization learning algorithm for mixture of experts

Text Categorization (TC)-the assignment of predefined categories to documents of a corpus-plays an important role in a wide variety of information organization and management tasks of Information Retrieval (IR). It involves the management of a lot of information, but some of them could be noisy or irrelevant and hence, a previous feature reduction could improve the performance of the classification. In this paper we proposed a wrapper approach. This kind of approach is time-consuming and sometimes could be infeasible. But our wrapper explores a reduced number of feature subsets and also it uses Support Vector Machines (SVM) as the evaluation system; and this two properties make the wrapper fast enough to deal with large number of features present in text domains. Taking the Reuters-21578 corpus, we also compare this wrapper with the common approach for feature reduction widely applied in TC, which consists of filtering according to scoring measures.