Scientific Research Publishing

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=47412#.VMib_izQrzE Author(s)   Rodrigo Morgon , Silvio do Lago Pereira Affiliation(s) Department of Information Technology, FATEC-SP/CEETEPS, S?o Paulo, Brazil . Department of Information Technology, FATEC-SP/CEETEPS, S?o Paulo, Brazil . ABSTRACT Concept learning is a kind of classification task that has interesting practical applications in several areas. In this paper, a new evolutionary concept learning algorithm is proposed and a corresponding learning system, called ECL ( Evolutionary Concept Learner ), is implemented. This system is compared to three traditional learning systems: MLP ( Multilayer Perceptron ), ID3 ( Iterative Dichotomiser ) and NB ( Naïve Bayes ). The comparison takes into account target concepts of varying complexities (e.g., with interacting attributes) and different qualities of training sets (e.g., with imbalanced classes and noisy class labe...

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52267#.VI-sxcnQrzE Author(s) Sergio Pissanetzky Affiliation(s) School of Science and Computer Engineering, University of Houston, Clear Lake, Texas, USA . ABSTRACT The big problem of Big Data is the lack of a machine learning process that scales and finds meaningful features. Humans fill in for the insufficient automation, but the complexity of the tasks outpaces the human mind’s capacity to comprehend the data. Heuristic partition methods may help but still need humans to adjust the parameters. The same problems exist in many other disciplines and technologies that depend on Big Data or Machine Learning. Proposed here is a frac...

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52267#.VI5IJcnQrzE \ Author(s) Sergio Pissanetzky Affiliation(s) School of Science and Computer Engineering, University of Houston, Clear Lake, Texas, USA . ABSTRACT The big problem of Big Data is the lack of a machine learning process that scales and finds meaningful features. Humans fill in for the insufficient automation, but the complexity of the tasks outpaces the human mind’s capacity to comprehend the data. Heuristic partition methods may help but still need humans to adjust the parameters. The same problems exist in many other disciplines and technologies that depend on Big Data or Machine Learning. Proposed here is a fr...

Read full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=51702#.VHVkW2fHRK0 Author(s) Hudson F. Golino 1* , Cristiano Mauro Assis Gomes 2 , Diego Andrade 2 Affiliation(s) 1 Núcleo de Pós-Graduacao, Pesquisa e Extensao, Faculdade Independente do Nordeste, Vitória da Conquista, Brazil . 2 Department of Psychology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil . ABSTRACT The present paper presents a relatively new non-linear method to predict academic achievement of high school students, integrating the fields of psychometrics and machine learning. A sample composed by 135 high-school students (10th grade, 50.34% boys), aged between 14 and 19 years old (M = 15.44, DP = 1.09), a...