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To explore the fusion of unstructured text data, the concept of entity networks has been proposed in many systems to facilitate analysis of entity relationship. Using appropriate visualization, entity networks could illustrate the relations of extracted entities. However, such a system generates too many entities and links from a single document, and many of them are trivial. With a complicated entity...
A series of experiments aimed to generate and learn fuzzy models for the valuation of residential premises was conducted using the KEEL tool (knowledge extraction based on evolutionary learning). Four regression and four post-processing algorithms were applied to several data sets. They referred to sales/purchase transactions of residential premises, which were derived from the cadastral system and...
Information extraction (IE) aims to extract from textual documents only the fragments which correspond to datafields required by the user. In this paper, we present new experiments evaluating a hybrid machine learning approach for IE that combines text classifiers and hidden Markov models (HMM). In this approach, a text classifier technique generates an initial output, which is refined by an HMM,...
In this work our aim is to increase the performance of fuzzy rule based classifications systems in the framework of imbalanced data-sets by means of the application of a genetic tuning step. We focus on the imbalanced data-set problem since it appears in many real application areas and, for this reason, it has become a relevant topic in the area of machine learning. This problem occurs when the number...
Many problems involve not structured environments which can be solved from the perspective of particle swarm optimization (PSO). In this research analyze the voting behavior in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a direct test of vote trading from logrolling. We show that they are...
Multi-objective meta-heuristics permit to conceive a complete novel approach to induce classifiers, where the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. Furthermore, these rules can be used as an unordered classifier, in this way, the rules are more intuitive and easier to understand...
This paper introduces a multiobjective grammar based genetic programming algorithm to solve a Web Mining problem from multiple instance perspective. This algorithm, called MOG3P-MI, is evaluated and compared with other available algorithms which extend a well-known neighborhood-based algorithm (k-nearest neighbour algorithm) and with a mono objective version of grammar guided genetic programming G3P-MI...
Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm...
For a given data set, different learning algorithms typically provide different classifiers. Although it is possible to simply select the most successful classifier, the less successful classifiers could have potentially valuable information that may be wasted. This work proposes GAESC, an algorithm for evolving a set of classifiers into a single symbolic classifier using genetic algorithms. Individuals...
This paper presents an efficient distributed genetic algorithm for classification rules extraction in data mining, which is based on a new method of dynamic data distribution applied to parallelism using networks of computers in order to mine large datasets. The presented algorithm shows many advantages when compared with other distributed algorithms proposed in the specific literature. In this way,...
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge...
This study addresses the adequacy of some theoretical information criteria when using finite mixture modelling on discovering patterns in continuous data. In fact, the selection of an adequate number of clusters is a key issue in deriving complex mixture structures and it is desirable that information criteria used for this end are effective. In order to select among several information criteria,...
The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing...
Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. This...
This paper addresses the problem of probability estimation in multiclass classification tasks combining two well known data mining techniques: support vector machines and neural networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs support vector machines within a one-vs-all reduction from multiclass to binary approach to obtain the distances...
Recommender systems attempt to predict the needs of Web users and provide them with recommendations to personalize their online experience. In this paper, we propose a neuro-fuzzy approach for the extraction of a recommendation model from usage data encoding user navigational behaviors. Such model is expressed as a set of fuzzy rules which may be exploited to provide personalized link suggestions...
Microarray technology allows to measure the expression levels of thousands of genes in an experiment. The use of computational methods is fundamental in cancer research. One of the possibilities is the use of artificial intelligence techniques. Several of these techniques have been used to analyze expression arrays. This paper presents a case-based reasoning (CBR) system for automatic classification...
XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve rule sets on-line by means of the interaction with an environment. Usually, research conducted on XCS has mainly focused on the analysis and improvement of the reinforcement learning component, overlooking the evolutionary discovery process to some extent. Recently, the first efforts...
The self-adaptive model of a XCS-based ensemble machine solving data-mining tasks has been presented. The results of experiments have shown the ability of the architecture to adapt the parameters of single XCS: the mutation rate mu and the tournament size ts- separately and/or together.
This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO2RBFN, for short-term forecasting of extra virgin olive oil price. The olive oil time series has been analyzed with a new evolutionary proposal for the design of RBFNs, CO2RBFN. Results obtained has been compared with ARIMA models and other data mining methods such as a fuzzy system developed with...
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