Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Two-way regionalized classification of multivariate datasets and its application to the assessment of hydrodynamic dispersion

Zones of mixing between shallow groundwaters of different composition were unravelled by two-way regionalized classification, a technique based on correspondence analysis (CA), cluster analysis (ClA) and discriminant analysis (DA), aided by gridding, map-overlay and contouring tools. The shallow groundwaters are from a granitoid plutonite in the Funda o region (central Portugal). Correspondence analysis detected three natural clusters in the working dataset: 1, weathering; 2, domestic effluents; 3, fertilizers. Cluster analysis set an alternative distribution of the samples by the three clusters. Group memberships obtained by correspondence analysis and by cluster analysis were optimized by discriminant analysis, gridded memberships as follows: codes 1, 2 or 3 were used when classification by correspondence analysis and cluster analysis produced the same results; code 0 when the grid node was first assigned to cluster 1 and then to cluster 2 or vice versa (mixing between weathering and effluents); code 4 in the other cases (mixing between agriculture and the other influences). Code-3 areas were systematically surrounded by code-4 areas, an observation attributed to hydrodynamic dispersion. Accordingly, the extent of code-4 areas in two orthogonal directions was assumed proportional to the longitudinal and transverse dispersivities of local soils. The results (0.7-16.8 and 0.4-4.3 m, respectively) are acceptable at the macroscopic scale. The ratios between longitudinal and transverse dispersivities (1.2-11.1) are also in agreement with results obtained by other studies.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Spurious shifts in the pattern of diurnal variation of sea level pressure of reanalysis datasets

The main purpose of this work is to report the presence of spurious discontinuities in the pattern of diurnal variation of sea level pressure of the three reanalysis datasets from: the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Science (R1), the NCEP and Department of Energy (R2), and the European Centre for Medium Range Weather Forecasting (ERA-40). Such discontinuities can be connected to the major changes in the global observing system that have occurred throughout reanalyses years. In the R1, the richest period in discontinuities is 1956-1958, coinciding with the start of modern radiosonde observation network. Rapid increase in the density of surface-based observations from 1967 also had an important impact on both R1 and ERA-40, with larger impact on R1. The reanalyses show discontinuities in the 1970s related to the assimilation of radiances measured by the Vertical Temperature Profile Radiometer and TIROS-N Operational Vertical Sounders onboard satellites. In the ERA-40, which additionally assimilated Special Sensor Microwave/Imager data, there are discontinuities in 1987-1989. The R1 also presents further discontinuities, in 1988-1993 likely connected to replacement/introduction of NOAA-series satellites with different biases, and to the volcanic eruption of Mount Pinatubo in June 1991, which is known to have severely affected measurements of infrared radiances for several years. The discontinuities in 1996-1998 might be partially connected to change in the type of radiosonde, from VIZ-B to VIZ-B2. The R2, which covers only satellite era (1979-on), shows discontinuities mainly in 1992, 1996-1997, and 2001. The discontinuities in 1992 and 2001 might have been caused by change in the satellite measurements and those in 1996-1997 by some changes in land-based observations network. © 2012 Springer-Verlag.

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)