Ádamo Lima de Santana

Strategies for improving the modeling and interpretability of Bayesian networks

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.

Multi-objective genetic algorithm for missing data imputation

The paper proposes a novel Multi-objective Genetic Algorithm for Data Imputation, called MOGAImp.This is the first method that applies a multi-objective approach to data imputation.MOGAImp presents a good tradeoff between the evaluation measures studied.The results confirm the MOGAImp prevalence for utilization over conflicting evaluation measures.MOGAImp codification scheme makes possible to adapt it to different application domains. A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values; five classifiers divided into three classes: rule induction learning, lazy learning and approximate models; and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains.

Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors

This paper presents a decision support system for power load forecast and the learning of influence patterns of the socio-economic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also presenting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data.

Análise discriminante múltipla do mercado varejista de açaí em Belém do Pará

Acai pulp is one of the main foods in the State of Para and of higher per capita consumption in 2010. The sale of acai in Belem is carried out by several units that operate in the retail market. The objective of this study was to identify market segments: greengrocers, micro entrepreneurs and supermarkets, from the analysis of a set of variables that define the decisions of acai consumers. The stepwise multiple discriminant analysis was used to estimate the discriminant functions. Results show that the segment identified with greater precision was the greengrocery, with 100%, followed by supermarkets, with 96.5% of the cases accurately classified, and the segment of micro entrepreneurs, with 90.9%, had the lowest accuracy in the consumer classification. The estimated model showed a high significance in the estimation process, since the functions with the variables amount of acai, consumer income, price of acai, quality of acai, price of fish and price of flour, correctly classified 95.6% of the original elements.

O valor econômico da extração manejada de madeira no baixo Amazonas, estado do Pará

O objetivo deste trabalho foi estimar o valor economico e a margem de comercializacao da madeira em tora oriunda de areas manejadas. Essa cadeia, alem de produzir margem positiva, os valores sao relativamente superiores aos gerados nas atividades que concorrem para o desmatamento na Amazonia para se estabelecerem. Adicionalmente, determinou-se o coeficiente medio de desdobramento de 37,3% (2,68 m3 de madeira em tora para cada 1,0 m3 de madeira serrada), o qual revelou baixo grau tecnologico das empresas. O valor economico medio da extracao e comercializacao da madeira em pe no mercado local foi de R

Algorithm for graphical Bayesian modeling based on multiple regressions

23,48/m3, tendo um valor minimo de R

A feasibility study of powerline communication technology for digital inclusion in Brazilian Amazon

9,06/m3 para as especies da categoria C4 (madeira branca) e um maximo de R

Performance Evaluation of Sentiment Analysis Methods for Brazilian Portuguese

55,63/m3 para as especies da categoria C1 (madeira especial). Assim, para um fluxo de 30 anos e extracao de 25 m3/ha nos planos de manejo das areas de concessao florestal do Estado do Para, gera-se um valor medio de R

An Evolutionary Missing Data Imputation Method for Pattern Classification

587,00/ha, ou R

Comparative studies of Statistical and Neural Networks Models for Short and Long Term Load Forecasting: a Case Study in the Brazilian Amazon Power Suppliers

19,56/ha/ano. No fluxo de 30 anos, esse resultado e relativamente superior ao gerado pelas atividades de pecuaria extensiva (em torno de R