3rd Workshop in Symbolic Data Analysis: book of abstracts

This workshop is the third regular meeting of researchers interested in Symbolic Data Analysis. The main aim of the event is to favor the meeting of people and the exchange of ideas from different fields - Mathematics, Statistics, Computer Science, Engineering, Economics, among others - that contribute to Symbolic Data Analysis

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Application of feature selection methods for automated clustering analysis : a review on synthetic datasets

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Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation

Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future

Machine learning applications for seismic processing and interpretation

During the past few years, exploration seismology has increasingly made use of machine learning algorithms in several areas including seismic data processing, attribute analysis, and computer aided interpretation. Since machine learning is a data-driven method for problem solving, it is important to adopt data which have good quality with minimal bias. Hidden variables and an appropriate objective function also need to be considered. In this dissertation, I focus my research on adapting machine learning algorithms that have been successfully applied to other scientific analysis problems to seismic interpretation and seismic data processing. Seismic data volumes can be extremely large, containing Gigabytes to Terrabytes of information. Add to these volumes the rich choice of seismic attributes, each of which has its own strengths in expressing geologic patterns, and the problem grows larger still. Seismic interpretation involves picking faults and horizons and identifying geologic features by their geometry, morphology, and amplitude patterns seen on seismic data. For the seismic facies classification task, I tested multiple attributes as input and built an attribute subset that can best differentiate the salt, mass transport deposits (MTDs), and conformal reflector seismic patterns using a suite of attribute selection algorithms. The resulting attribute subset differentiates the three classes with high accuracy and has the benefit of reducing the dimensionality of the data. To maximize the use of unlabeled data as well as labeled data, I provide a workflow for facies classification based on a semi-supervised learning approach. Compared to using only labeled data, I find that the addition of unlabeled data for learning results in higher performance of classification.. In seismic processing, I propose a deep learning approach for random and coherent noise attenuation in the frequency – space domain. I find that the deep ResNet architecture speeds up the process of denoising and improves the accuracy, which efficiently separates the noise from signals. Finally, I review geophysical inversion and machine learning approaches in an aspect of solving inverse problems and show similarities and differences of these approaches in both mathematical formulation and numerical tests