Gláucia M. Bressan

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed.

BayesRule: A Markov-Blanket based procedure for extracting a set of probabilistic rules from Bayesian classifiers

A Bayesian network (BN) is a formalism for representing and reasoning about uncertain domains. In BNs the knowledge is represented by a combination of a graph-based structure and probability theory. A particular type of BN known as Bayesian Classifier (BC) aims at classifying a given instance into a discrete class. BCs have been intensively used for knowledge modeling in many different applications and have been the focus of many works related to data mining. Data mining tasks are usually applied to real domains having large number of variables. In such domains, the classifiers tend to be large and complex and consequently are not so easily understood by human beings. This paper proposes an approach for promoting the understandability of the knowledge represented by a BC, by translating it into a more convenient and easily understandable form of representation, that of classification rules. The proposed method named BayesRule uses the concept of Markov-Blanket to obtain a reduced set of rules in relation to both, the number of rules and the number of conditions in the antecedent of a rule. Experiments using seven knowledge domains show that the reduced set of rules extracted from a BC can be smaller and still maintain the BC classification accuracy.

Markov-Blanket Based Strategy for Translating a Bayesian Classifier into a Reduced Set of Classification Rules

Bayesian network (BN) is a formalism for representing and reasoning about uncertain domains. In BN the knowledge is represented by a combination of a graph-based structure and probability theory. A particular type of BN known as Bayesian Classifier (BC) aims at classifying a given instance into a discrete class. BCs have been extensively used for modeling knowledge in many different applications and have been the focus of many works related to data mining. Depending on the size of a BC the understandability of the knowledge it represents is not an easy task. This paper proposes an approach to help the process of understanding the knowledge represented by a BC, by translating it into a more convenient and easily understandable form of representation, that of classification rules. The proposed method named BayesRule (BR) uses the concept of Markov Blanket (MB) to obtain a reduced set of rules in respect to both, the number of rules and the number of antecedents in rules. Experiments using the ALARM network showed that the reduced set of rules extracted from the BC can be smaller than the set of rules representing a decision tree generated by C4.5, and still maintains a high accuracy rate.

Biomass Based Weed-Crop Competitiveness Classification Using Bayesian Networks

This paper describes the modeling of a biomass based weed-crop competitiveness classification process, based on a Bayesian network classifier. The understandability of the model is improved by its automatic translation into a set of classification rules, which are easily understood by human beings. The Bayes approach is based on empirical data collected in a corn-crop and uses the concept of maximum a posteriori probability to extract a set of probabilistic rules from the induced Bayesian network classifier. The features used to build the Bayesian network classifier are the total density of weeds and the corresponding proportions of narrow and broadleaf weeds and the class variable is the weeds biomass from which the weed-crop competitiveness is inferred. The paper presents a set of 27 rules extracted from the Bayesian network classifier which classify the biomass of weeds.

Sistema de classificação fuzzy para o risco de infestação por plantas daninhas considerando a sua variabilidade espacial

Este artigo trata do problema de classificacao do risco de infestacao por plantas daninhas usando tecnicas geoestatisticas, analise de imagens e modelos de classificacao fuzzy. Os principais atributos utilizados para descrever a infestacao incluem a densidade de sementes, bem como a sua extensao, a cobertura foliar e a agressividade das plantas daninhas em cada regiao. A densidade de sementes reflete a producao de sementes por unidade de area, e a sua extensao, a influencia das sementes vizinhas; a cobertura foliar indica a extensao dos agrupamentos das plantas daninhas emergentes; e a agressividade descreve a porcentagem de ocupacao de especies com alta capacidade de producao de sementes. Os dados da densidade de sementes, da cobertura foliar e da agressividade para as diferentes regioes sao obtidos a partir de simulacao com modelos matematicos de populacoes. Neste artigo propoe-se um sistema de classificacao fuzzy utilizando os atributos descritos para inferir os riscos de infestacao de regioes da cultura por plantas daninhas. Resultados de simulacao sao apresentados para ilustrar o uso desse sistema na aplicacao localizada de herbicida.

MINIMIZING THE PREPARATION TIME OF A TUBES MACHINE: EXACT SOLUTION AND HEURISTICS

In this paper we optimize the preparation time of a tubes machine. Tubes are hard tubes made by gluing strips of paper that are packed in paper reels, and some of them may be reused between the production of one and another tube. We present a mathematical model for the minimization of changing reels and movements and also implementations for the heuristics Nearest Neighbor, an improvement of a nearest neighbor (Best Nearest Neighbor), refinements of the Best Nearest Neighbor heuristic and a heuristic of permutation called Best Configuration using the IDE (integrated development environment) WxDev C++. The results obtained by simulations improve the one used by the company.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss.

Reordenamento eficiente das colunas básicas na programação de lotes e cortes

In this work the combined problem is considered, which solves simultaneously the lot sizing and the cutting stock problems. We study some properties of the matrix of constraints and how to factorize the base without losing sparsity in the simplex method context, by a static reordering of the basic columns. Numerical results simulating simplex iterations and verify the sparsity of the factorizations are presented. Numerical experiments had also proven the robustness of this strategy. We conclude that the approach of constructing of the static sparse base reordering leads to very good computational results for both: speed and robustness, in comparison with approaches which do not consider the sparse structure of the matrix of constraints.

Atualização Eficiente da Decomposição LU na Programação de Lotes e Cortes

Neste trabalho consideramos o Problema Combinado, que esta baseado na decisao de antecipar ou nao a producao de certos produtos finais.