Julio Cesar Nievola
Other affiliations: The Catholic University of America
Bio: Julio Cesar Nievola is an academic researcher from Pontifícia Universidade Católica do Paraná. The author has contributed to research in topics: Feature selection & Artificial neural network. The author has an hindex of 12, co-authored 74 publications receiving 757 citations. Previous affiliations of Julio Cesar Nievola include The Catholic University of America.
Papers published on a yearly basis
•07 Jul 2001
••09 Jul 2002
TL;DR: In this paper, a genetic programming algorithm is proposed for attribute construction, which constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm.
Abstract: For a given data set, its set of attributes defines its data space representation. The quality of a data space representation is one of the most important factors influencing the performance of a data mining algorithm. The attributes defining the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem, this paper proposes a Genetic Programming algorithm developed for attribute construction. This algorithm constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm.
03 Sep 2001
TL;DR: The basic idea is that the individual encoding scheme incorporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form, which has some innovative ideas with respect to the encoding of GP individuals representing rule sets.
Abstract: In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership function definitions. The two populations co-evolve, so that the final result of the co-evolutionary process is a fuzzy rule set and a set of membership function definitions which are well adapted to each other. In addition, our system also has some innovative ideas with respect to the encoding of GP individuals representing rule sets. The basic idea is that our individual encoding scheme incorporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form. We have also adapted GP operators to better work with the proposed individual encoding scheme.
11 May 2000
TL;DR: This work presents a method for extracting accurate, comprehensible rules from neural networks using a genetic algorithm to find a good neural network topology and passes this topology to a rule extraction algorithm, and the quality of the extracted rules is fed back to the genetic algorithm.
Abstract: A common problem in KDD (Knowledge Discovery in Databases) is the presence of noise in the data being mined. Neural networks are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. However, they have the well-known disadvantage of not discovering any high-level rule that can be used as a support for human decision making. In this work we present a method for extracting accurate, comprehensible rules from neural networks. The proposed method uses a genetic algorithm to find a good neural network topology. This topology is then passed to a rule extraction algorithm, and the quality of the extracted rules is then fed back to the genetic algorithm. The proposed system is evaluated on three public-domain data sets and the results show that the approach is valid.
••24 Jul 2016
TL;DR: The pairwise approach is effective to discriminate between different facial expressions and the results achieved by the proposed approach are slightly better than several current approaches.
Abstract: This paper proposes a novel approach that combines specialized pairwise classifiers trained with different feature subsets for facial expression classification. The proposed approach first detects and extracts automatically faces from images. Next, the face is split into several regular zones and textural features are extracted from each zone to capture local information. The features extracted from all zones are concatenated to model the whole face. A pairwise approach that considers all pairs of classes and a hybrid feature selection strategy is used to both reduce the dimensionality and to select relevant features to discriminate between specific pairs of classes. Several pairwise classifiers are then trained with such pairwise feature subsets. At the end, given a new face image, all features are extracted from such a face, but only the previously selected subset of features is inputted to each pairwise classifier. The output of all pairwise classifiers is combined using a majority voting rule to decide on the facial expression. Experiments have been carried out on three publicly available datasets (JAFFE, CK and TFEID) and the correct classification rates of 99.05%, 98.07% and 99.63% were achieved respectively. Therefore, the pairwise approach is effective to discriminate between different facial expressions and the results achieved by the proposed approach are slightly better than several current approaches.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
26 Mar 2008
TL;DR: A unique overview of this exciting technique is written by three of the most active scientists in GP, which starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination until high-fitness solutions emerge.
Abstract: Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.
01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)