Comparison for three Classification Techniques

In this paper we show the results of a comparison simulation study for three classification techniques: Multinomial Logistic Regression (MLR), No Metric Discriminant Analysis (NDA) and Linear Discriminant Analysis (LDA). The measure used to compare the performance of the three techniques was the Error Classification Rate (ECR). We found that MLR and LDA techniques have similar performance and that they are better than DNA when the population multivariate distribution is Normal or Logit-Normal. For the case of log-normal and Sinh(-1)-normal multivariate distributions we found that MLR had the better performance.

Chagas disease-related mortality trends, state of Sao Paulo, Brazil, 1985 to 2006: a study using multiple causes of death

Objectives. To study mortality trends related to Chagas disease taking into account all mentions of this cause listed on any line or part of the death certificate. Methods. Mortality data for 1985-2006 were obtained from the multiple cause-of-death database maintained by the Sao Paulo State Data Analysis System (SEADE). Chagas disease was classified as the underlying cause-of-death or as an associated cause-of-death (non-underlying). The total number of times Chagas disease was mentioned on the death certificates was also considered. Results. During this 22-year period, there were 40 002 deaths related to Chagas disease: 34 917 (87.29%) classified as the underlying cause-of-death and 5 085 (12.71%) as an associated cause-of-death. The results show a 56.07% decline in the death rate due to Chagas disease as the underlying cause and a stabilized rate as associated cause. The number of deaths was 44.5% higher among men. The fact that 83.5% of the deaths occurred after 45 years of age reflects a cohort effect. The main causes associated with Chagas disease as the underlying cause-of-death were direct complications due to cardiac involvement, such as conduction disorders, arrhythmias and heart failure. Ischemic heart disease, cerebrovascular disorders and neoplasms were the main underlying causes when Chagas was an associated cause-of-death. Conclusions. For the total mentions to Chagas disease, a 51.34% decline in the death rate was observed, whereas the decline in the number of deaths was only 5.91%, being lower among women and showing a shift of deaths to older age brackets. Using the multiple cause-of-death method contributed to the understanding of the natural history of Chagas disease.

Paracoccidioidomycosis-related mortality trend, state of Sao Paulo, Brazil: a study using multiple causes of death

Objective. To investigate mortality in which paracoccidioidomycosis appears on any line or part of the death certificate. Method. Mortality data for 1985-2005 were obtained from the multiple cause-of-death database maintained by the Sao Paulo State Data Analysis System (SEADE). Standardized mortality coefficients were calculated for paracoccidioidomycosis as the underlying cause-of-death and as an associated cause-of-death, as well as for the total number of times paracoccidioidomycosis was mentioned on the death certificates. Results. During this 21-year period, there were 1950 deaths related to paracoccidioidomycosis; the disease was the underlying cause-of-death in 1 164 cases (59.69%) and an associated cause-of-death in 786 (40.31%). Between 1985 and 2005 records show a 59.8% decline in the mortality coefficient due to paracoccidioidomycosis as the underlying cause and a 53.0% decline in the mortality as associated cause. The largest number of deaths occurred among men, in the older age groups, and among rural workers, with an upward trend in winter months. The main causes associated with paracoccidioidomycosis as the underlying cause-of-death were pulmonary fibrosis, chronic lower respiratory tract diseases, and pneumonias. Malignant neoplasms and AIDS were the main underlying causes when paracoccidioidomycosis was an associated cause-of-death. The decision tables had to be adapted for the automated processing of causes of death in death certificates where paracoccidioidomycosis was mentioned. Conclusions. Using the multiple cause-of-death method together with the traditional underlying cause-of-death approach provides a new angle on research aimed at broadening our understanding of the natural history of paracoccidioidomycosis.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters

Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO (4) (3-) , and SO (4) (2-) concentrations were as follows: Cu (3.1-120 A mu g L(-1)), Fe (60-330 A mu g L(-1)), Mg (48-123 mg L(-1)), Mn (0.4-4.0 mg L(-1)), PO (4) (3-) (55-212 mg L(-1)), and SO (4) (2-) (19-136 mg L(-1)). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey-Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.