Modeling The Complex Land Administration in Brazil

Land administration is one of the pillars of economic development and poverty reduction. Land registration and cadastres make up an important part of land administration. In Brazil, recent initiatives attempt to build an efficient land administration to overcome its deficiencies built from a history of disorderly occupation and with many specificities of a colonial past. The objective of this paper is to present the land registration process in urban areas in Brazil using modeling of land administration. The purpose is to present in a model the procedures for three scenarios of the registration and transfer of urban properties: 1. Procedures for transfer a formally registered; 2. Procedures for registration of a semi-formal property (individual proceeding); and 3. Procedures for the registration of an informal settlement (collective proceeding). From the models it was possible to visualize the complexity of the procedures of registration and transfer of a property in the urban area. The procedures usually have many steps, many actors involved, it requires a lot of time and it has high costs. In addition, the procedures show the absence of an urban cadastre that supports land registration. In conclusion, in Brazil, despite recent developments of legal framework and practices related to land, does not have a complete land administration system. The legal framework is extensive and often contradictory, processes are complex, expensive and take long, and there is still a long way until land and the information about land may be effectively managed

Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime

Distance-based decision tree algorithms for label ranking

The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have developed/adapted to treat rankings as the target object follow two different approaches: distribution-based (e.g., using Mallows model) or correlation-based (e.g., using Spearman’s rank correlation coefficient). Decision trees have been adapted for label ranking following both approaches. In this paper we evaluate an existing correlation-based approach and propose a new one, Entropy-based Ranking trees. We then compare and discuss the results with a distribution-based approach. The results clearly indicate that both approaches are competitive

Classification tree analysis of second neoplasms in survivors of childhood cancer

BACKGROUND: Reports on childhood cancer survivors estimated cumulative probability of developing secondary neoplasms vary from 3,3% to 25% at 25 years from diagnosis, and the risk of developing another cancer to several times greater than in the general population. METHODS: In our retrospective study, we have used the classification tree multivariate method on a group of 849 first cancer survivors, to identify childhood cancer patients with the greatest risk for development of secondary neoplasms. RESULTS: In observed group of patients, 34 develop secondary neoplasm after treatment of primary cancer. Analysis of parameters present at the treatment of first cancer, exposed two groups of patients at the special risk for secondary neoplasm. First are female patients treated for Hodgkin's disease at the age between 10 and 15 years, whose treatment included radiotherapy. Second group at special risk were male patients with acute lymphoblastic leukemia who were treated at the age between 4,6 and 6,6 years of age. CONCLUSION: The risk groups identified in our study are similar to the results of studies that used more conventional approaches. Usefulness of our approach in study of occurrence of second neoplasms should be confirmed in larger sample study, but user friendly presentation of results makes it attractive for further studies

Mining association rules for label ranking

Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

MetaBags: Bagged Meta-Decision Trees for Regression

Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although critical, methods for learning heterogeneous regression ensembles have not been proposed at large scale, whereas in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel, practically useful stacking framework for regression. MetaBags is a meta-learning algorithm that learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. A set of meta-decision trees are learned using different types of meta-features, specially created for this purpose - to then be bagged at meta-level. This procedure is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on synthetic, open and real-world application datasets. The obtained results show that our method significantly outperforms existing state-of-the-art approaches

An Abundant Dysfunctional Apolipoprotein A1 in Human Atheroma

Recent studies have indicated that high-density lipoproteins (HDLs) and their major structural protein, apolipoprotein A1 (apoA1), recovered from human atheroma are dysfunctional and are extensively oxidized by myeloperoxidase (MPO). In vitro oxidation of either apoA1 or HDL particles by MPO impairs their cholesterol acceptor function. Here, using phage display affinity maturation, we developed a high-affinity monoclonal antibody that specifically recognizes both apoA1 and HDL that have been modified by the MPO-H2O2-Cl− system. An oxindolyl alanine (2-OH-Trp) moiety at Trp72 of apoA1 is the immunogenic epitope. Mutagenesis studies confirmed a critical role for apoA1 Trp72 in MPO-mediated inhibition of the ATP-binding cassette transporter A1 (ABCA1)-dependent cholesterol acceptor activity of apoA1 in vitro and in vivo. ApoA1 containing a 2-OH-Trp72 group (oxTrp72-apoA1) is in low abundance within the circulation but accounts for 20% of the apoA1 in atherosclerosis-laden arteries. OxTrp72-apoA1 recovered from human atheroma or plasma is lipid poor, virtually devoid of cholesterol acceptor activity and demonstrated both a potent proinflammatory activity on endothelial cells and an impaired HDL biogenesis activity in vivo. Elevated oxTrp72-apoA1 levels in subjects presenting to a cardiology clinic (n = 627) were associated with increased cardiovascular disease risk. Circulating oxTrp72-apoA1 levels may serve as a way to monitor a proatherogenic process in the artery wall