Jesús Ruiz

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Papers by Jesús Ruiz

Data set Editing by Ordered Projection

Intelligent Data Analysis, 2001

In this paper, an editing algorithm based on the projection of the examples in each dimension is ... more In this paper, an editing algorithm based on the projection of the examples in each dimension is presented. The algorithm, that we have called EOP (Editing by Ordered Projection) has some interesting characteristics: important reduction of the number of examples from the database; lower computational cost in respect of other typical algorithms due to the absence of distance calculations; conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classifiers; reduction of the decision tree size or the number of decision rules. The performance of EOP is showed by comparing the results provided by C4.5 [5] before and after applying it on databases with continuous attributes. These experiments have been realised using some databases from UCI repository [1]. The use of EOP as preprocessing method for the later application of any axis-parallel learning algorithm convert it in a valuable tool in the field of data mining.

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Incremental Rule Learning and Border Examples Selection from Numerical Data Streams

J. Univers. Comput. Sci., 2005

Mining data streams is a challenging task that requires online systems ba- sed on incremental lea... more Mining data streams is a challenging task that requires online systems ba- sed on incremental learning approaches. This paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unneces- sary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbour algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.

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Is it Possible to Find a Pattern of k for the Nearest Neighbor?

Algoritmo de Aprendizaje de Reglas de Decisión a partir de Flujos de Datos Transaccionales

Non-parametric Nearest Neighbor with Local Adaptation

Lecture Notes in Computer Science, 2001

The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the param... more The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified most examples. As the user does not take part in the election of the parameter k, the algorithm is non-parametric. With this heuristic, we propose an easy variation of the k-NN algorithm that gives robustness with noise present in data. Summarized in the last section, the experiments show that the error rate decreases in comparison with the k-NN technique when the best k for each database has been previously obtained.

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Prototype-based mining of numeric data streams

Proceedings of the 2003 ACM symposium on Applied computing - SAC '03, 2003

Great organizations collect open-ended and time-changing data received at a high speed. The possi... more Great organizations collect open-ended and time-changing data received at a high speed. The possibility of extracting useful knowledge from these potentially infinite databases is a new challenge in Data Mining. In this paper we propose an anytime incremental learning algorithm for mining numeric data streams. Within Supervised Learning, our approach is based on prototypes and hypercubic decision rules, concerning with the simplicity of the model provided and the time complexity as primary goals. Experimental results with synthetic databases of 100 gigabytes show a good performance from streams of data in continuous transformation.

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Statistical Test-Based Evolutionary Segmentation of Yeast Genome

Genetic and Evolutionary Computation – GECCO 2004, 2004

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Evolutionary Search of Biclusters by Minimal Intrafluctuation

2007 IEEE International Fuzzy Systems Conference, 2007

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Natural Encoding for Evolutionary Supervised Learning

IEEE Transactions on Evolutionary Computation, 2007

Some of the most influential factors in the quality of the solutions found by an evolutionary alg... more Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a correct coding of the search space and an appropriate evaluation function of the potential solutions. EAs are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding," uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions. Experiments with several datasets from the University of California at Irvine (UCI) machine learning repository show that as the genetic operators are better guided through the search space, the number of rules decreases considerably while maintaining the accuracy, similar to that of hybrid coding, which joins the well-known binary and real representations to encode discrete and continuous attributes, respectively. The computational cost associated with the natural coding is also reduced with regard to the hybrid representation. Our algorithm, HIDER*, has been statistically tested against C4.5 and C4.5 Rules, and performed well. The knowledge models obtained are simpler, with very few decision rules, and therefore easier to understand, which is an advantage in many domains. The experiments with high-dimensional datasets showed the same good behavior, maintaining the quality of the knowledge model with respect to prediction accuracy. Index Terms-Decision rules, evolutionary encoding, supervised learning. I. INTRODUCTION D ECISION RULES are especially relevant in problems related to supervised learning. Given a dataset with continuous and discrete features or attributes, and a class label, we try to find a rule set that describes the knowledge within data or classifies new unseen data. When the feature is discrete, the rules take the form of "if , then class," where the values are not necessarily all those that the feature can take. When the feature is continuous, typically the rules take the form of "if then class," where

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Mining Low Dimensionality Data Streams of Continuous Attributes

Lecture Notes in Computer Science, 2003

This paper presents an incremental and scalable learning algorithm in order to mine numeric, low ... more This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high-cardinality, time-changing data streams. Within the Supervised Learning field, our approach, named SCALLOP, provides a set of decision rules whose size is very near to the number of concepts to be extracted. Experimental results with synthetic databases of different complexity degrees show a good performance from streams of data received at a rapid rate, whose label distribution may not be stationary in time.

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Mining low dimensionality data streams of continuous attributes

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Connecting segments for visual data exploration and interactive mining of decision rules

Visualization has become an essential support throughout the KDD process in order to extract hidd... more Visualization has become an essential support throughout the KDD process in order to extract hidden information from huge amount of data. Visual data exploration techniques provide the user with graphic views or metaphors that represent potential patterns and data relationships. However, an only image does not always convey high-dimensional data properties successfully. From such data sets, visualization techniques have to deal with the curse of dimensionality in a critical way, as the number of examples may be very small with respect to the number of attributes. In this work, we describe a visual exploration technique that automatically extracts relevant attributes and displays their ranges of interest in order to support two data mining tasks: classification and feature selection. Through different metaphors with dynamic properties, the user can re-explore meaningful intervals belonging to the most relevant attributes, building decision rules and increasing the model accuracy interactively.

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Classification Model for Data Streams Based on Similarity

Lecture Notes in Computer Science, 2011

Mining data streams is a field of study that poses new challenges. This research delves into the ... more Mining data streams is a field of study that poses new challenges. This research delves into the study of applying different techniques of classification of data streams, and carries out a comparative analysis with a proposal based on similarity; introducing a new form of management of representative data models and policies of insertion and removal, advancing also in the design

An evolutionary approach to estimating software development projects

Information and Software Technology, 2001

The use of dynamic models and simulation environments in connection with software projects paved ... more The use of dynamic models and simulation environments in connection with software projects paved the way for tools that allow us to simulate the behaviour of the projects. The main advantage of a Software Project Simulator (SPS) is the possibility of experimenting with different decisions to be taken at no cost. In this paper, we present a new approach based on the combination of an SPS and Evolutionary Computation. The purpose is to provide accurate decision rules in order to help the project manager to take decisions at any time in the development. The SPS generates a database from the software project, which is provided as input to the evolutionary algorithm for producing the set of management rules. These rules will help the project manager to keep the project within the cost, quality and duration targets. The set of alternatives within the decision-making framework is therefore reduced to a quality set of decisions. q

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Natural Encoding for Evolutionary Supervised Learning

by Jesús Ruiz and R. Giraldez

IEEE Transactions on Evolutionary Computation, 2000

One of the most influential factors in the quality of the solutions found by an evolutionary algo... more One of the most influential factors in the quality of the solutions found by an evolutionary algorithm are a correct coding of the search space and an appropriate evaluation function of the potential solutions. Evolutionary algorithms are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding", uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions.

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