Search NASA⌕ Search

SEARCH · Search NASA

Results for “Bayesian classification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Spline Classification Methods

The use of spline functions in the development of classification algorithms is discussed. A method is formulated for producing spline approximations to univariate density functions when each density function is described by a histogram of measurements. The resulting approximations are then incorporated into a Bayesian classification procedure for which the probability of misclassification can be readily computed. Some preliminary numerical results are presented to illustrate the method.

Guseman, L. F., Jr.↗
NASA NTRS1993

Classification Of Terrain In Polarimetric SAR Images

Two algorithms processing polarimetric synthetic-aperture-radar data found effective in assigning various parts of SAR images to classes representing different types of terrain. Partially automate interpretation of SAR imagery, reducing amount of photointerpretation needed and putting whole interpretation process on more quantitative and systematic basis. First algorithm implements Bayesian classification scheme "supervised" by use of training data. Second algorithm implements classification procedure unsupervised.

Van Zyl, Jakob J.↗
NASA NTRS1983

The statewide forest/nonforest classification of Pennsylvania using Landsat MSS data

A procedure is described for processing the large volume of data needed to generate a Landsat-derived forest resource map (forest/nonforest mask) for the state of Pennsylvania, for use in a defoliation assessment program. Landsat coverage of the 28 million acres encompassing Pennsylvania requires portions of ten Landsat frames, totalling approximately 76 million pixels. A prime effort of this project was to efficiently and accurately classify this data into forest or nonforest categories. A specialized approach to Bayesian classification was developed which involved the use of MSS5 and MSS7; the identification of a single class, forest; and the use of the confidence map to generate the forest/nonforest mask. Statewide, for 7-acre contiguous forest areas, the overall agreement achieved between the mask and reference data was 90 percent.

Russo, S. A.↗
DOE OSTI2017

Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring

The presence of faults/ fractures or highly permeable zones in the primary sealing caprock of a CO2 storage reservoir can result in leakage of CO2. Monitoring of leakage requires the capability to detect and resolve the onset, location, and volume of leakage in a systematic and timely manner. Pressure-based monitoring possesses such capabilities. This study demonstrates a basis for monitoring network design based on the characterization of CO2 leakage scenarios through an assessment of the integrity and permeability of the caprock inferred from above zone pressure measurements. Four representative heterogeneous fractured seal types are characterized to demonstrate seal permeability ranging from highly permeable to impermeable. Based on Bayesian classification theory, the probability of each fractured caprock scenario given above zone pressure measurements with measurement error is inferred. The sensitivity to injection rate and caprock thickness is also evaluated and the probability of proper classification is calculated. The time required to distinguish between above zone pressure outcomes and the associated leakage scenarios is also computed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-49: Automatic Beam Angle Determination for Lung IMRT Planning Using a Beam Configuration Atlas

Purpose: To present a technique to automatically determine beam angle configurations for lung IMRT planning based on the patient-specific anatomy and tumor geometry. Methods: The relationship between individual patient anatomy and proper beam configurations was learned from high quality clinical plans in three steps. First, a beam configuration atlas was obtained by classifying 60 lung IMRT plans into 6 beam configuration clusters based on a dissimilarity measure defined between different beam configurations. A beam configuration template was extracted from each cluster to form an atlas. Second, a beam efficiency index map (EI map) was constructed to characterize the geometry of the tumor relative to the lungs, the body and other OARs along each candidate beam direction. Finally, the EI maps of the clinical cases and the cluster assignments of their beam configurations were paired to train a Bayesian classification model. This technique was validated by leave-one-out cross validation with 16 cases randomly selected from the original dataset. An IMRT plan (autobeam plan) for each test case was generated using the beam configuration template according to the cluster assignment given by the model and was compared with the corresponding clinical plan. Results: The dosimetric parameters (mean±S.D. in percentage of prescription dose) in the auto-beam plans and in the clinical plans, respectively, and the p-values by a paired ttest (in parenthesis) are: lung Dmean: 16.3±9.3, 18.6±7.4 (0.48), esophagus Dmean: 28.4±18, 30.7±19.3 (0.02), Heart Dmean: 21.5±17.5,21.1±17.2 (0.76), Spinal Cord D2%: 48±23, 51.2±21.8 (0.01), PTV dose homogeneity (D2%–D99%): 22±27.4, 20.4±12.8 (0.10).The dose reductions by the autobeam plans in esophagus Dmean and cord D02 are statistically significant but the differences (<4%) may not be clinically significant. The other dosimetric parameters are not statistically different. Conclusion: Plans generated by the automatic beam angle determination method can achieve dosimetric quality equivalent to that of clinical plans. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

An ensemble model of QSAR tools for regulatory risk assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

The spectral energy distributions of Fermi blazars

In this paper, multiwavelength data are compiled for a sample of 1425 Fermi blazars to calculate their spectral energy distributions (SEDs). A parabolic function, log(νF{sub ν})=P{sub 1}(logν−P{sub 2}){sup 2}+P{sub 3}, is used for SED fitting. Synchrotron peak frequency (logν{sub p}), spectral curvature (P{sub 1}), peak flux (ν{sub p}F{sub ν{sub p}}), and integrated flux (νF{sub ν}) are successfully obtained for 1392 blazars (461 flat-spectrum radio quasars [FSRQs], 620 BL Lacs [BLs], and 311 blazars of uncertain type [BCUs]; 999 sources have known redshifts). Monochromatic luminosity at radio 1.4 GHz, optical R band, X-ray at 1 keV and γ-ray at 1 GeV, peak luminosity, integrated luminosity, and effective spectral indices of radio to optical (α{sub RO}) and optical to X-ray (α{sub OX}) are calculated. The “Bayesian classification” is employed to log ν{sub p} in the rest frame for 999 blazars with available redshift, and the results show that three components are enough to fit the log ν{sub p} distribution; there is no ultra-high peaked subclass. Based on the three components, the subclasses of blazars using the acronyms of Abdo et al. are classified, and some mutual correlations are also studied. Conclusions are finally drawn as follows: (1) SEDs are successfully obtained for 1392 blazars. The fitted peak frequencies are compared with common sources from available samples. (2) Blazars are classified as low synchrotron peak sources if log ν{sub p}(Hz)⩽14.0, intermediate synchrotron peak sources if 14.0< log ν{sub p}(Hz)⩽15.3, and high synchrotron peak sources if log ν{sub p}(Hz)>15.3. (3) Gamma-ray emissions are strongly correlated with radio emissions. Gamma-ray luminosity is also correlated with synchrotron peak luminosity and integrated luminosity. (4) There is an anticorrelation between peak frequency and peak luminosity within the whole blazar sample. However, there is a marginally positive correlation for high synchrotron peak BL Lacs (HBLs), and no correlations for FSRQs or low synchrotron peak BL Lacs (LBLs). (5) There are anticorrelations between the monochromatic luminosities (γ-ray and radio bands) and the peak frequency within the whole sample and BL Lacs. (6) The optical to X-ray (α{sub OX}) and radio to optical (α{sub RO}) spectral indices are strongly anticorrelated with peak frequency (log ν{sub p}) within the whole sample, but the correlations for subclasses of FSRQs, LBLs, and HBLs are different.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Adaptive statistical pattern classifiers for remotely sensed data

A technique for the adaptive estimation of nonstationary statistics necessary for Bayesian classification is developed. The basic approach to the adaptive estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest and (2) a projection of the parameters in time or position. A divergence criterion is developed to monitor algorithm performance. Comparative results of adaptive and nonadaptive classifier tests are presented for simulated four dimensional spectral scan data.

Gonzalez, R. C.↗
NASA NTRS1984

Nonparametric analysis of Minnesota spruce and aspen tree data and LANDSAT data

The application of nonparametric methods in data-intensive problems faced by NASA is described. The theoretical development of efficient multivariate density estimators and the novel use of color graphics workstations are reviewed. The use of nonparametric density estimates for data representation and for Bayesian classification are described and illustrated. Progress in building a data analysis system in a workstation environment is reviewed and preliminary runs presented.

Scott, D. W.↗
NASA NTRS1988

Technology forecast and applications for autonomous, intelligent systems

Significant research products which have emerged from the core program of NASA's Office of Aeronautics and Space Technology (OAST) are discussed. The Space Station Thermal Control System, the Space Shuttle Integrated Communications Officer Station, the Launch Processing System, the Expert Scheduling System for Pioneer Venus Spacecraft, a Bayesian classification system, and a spaceborne multiprocessor system are included. The technology trends which led to these results are discussed and future developments in technology are forecasted.

Lum, Henry, Jr.↗
DOE OSTI2019

Scalable Bayesian Nonparametric Clustering and Classification

We develop a scalable multi-step Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach makes inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating data sets: a large set of electronic health records (EHR) and a bank telemarketing dataset. We find interesting clusters and competitive classification performance relative to other widely used competing classifiers. Supplementary materials for this article are available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Bayesian high-redshift quasar classification from optical and MID-IR photometry

We identify 885,503 type 1 quasar candidates to i≲22 using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer (WISE) “AllWISE” data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type 1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with 3.5 3.5 than the traditional mid-IR selection “wedges” and to 2.2 3. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Classifier-guided sampling for discrete variable, discontinuous design space exploration: Convergence and computational performance

A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodeling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates nondifferentiable and discontinuous functions of discrete or categorical variables. The CGS method uses these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill-suited for conventional metamodeling techniques and too computationally expensive to be solved by population-based algorithms alone. In addition, the rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.

42 ENGINEERING↗
NASA NTRS2005

Elemental Abundance Distributions in Basalt Clays and Meteorites: Is It a Biosignature?

Volcanic glass altered by microorganisms exhibits distinctive textures differing significantly from abiotic alteration [1-4]. We have previously presented morphological evidence of bioweathering in sub-oceanic basalt glass [5] and olivine [6], and noted similar alterations in Nakhla [7]. We have also introduced an autonomous Bayesian probabilistic classification methodology to identify biotic and abiotic alteration in sub-oceanic basalts using elemental abundance data [8]. We now present data from multiple sub-oceanic sites addressing the more general question of utilizing elemental abundance distribution in clays as a valid biosignature for the exploration of putative clay alteration products in meteorites.

Fisk, M. R.↗
NASA NTRS1975

A hybrid classifier using the parallelepiped and Bayesian techniques

A versatile classification scheme is developed which uses the best features of the parallelepiped algorithm and the Bayesian maximum likelihood algorithm. The parallelepiped technique has the advantage of being very fast, especially when implemented into a table look-up scheme; its disadvantage is its inability to distinguish and classify spectral signatures which are similar in nature. This disadvantage is eliminated by the Bayesian technique which is capable of distinguishing subtle differences very well. The hybrid algorithm developed reduces computer time by as much as 90%. A two- and n-dimensional description of the hybrid classifier is given.

Addington, J. D.↗
NASA NTRS2005

Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms

Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures

Storrie-Lombardi, Michael C.↗
NASA NTRS1979

Classification results using spacially correlated Landsat data

Tubbs and Coberly (1978) demonstrated that Landsat multispectral scanner data are not independent random observations, but, are in fact highly correlated. They also demonstrated that the correlation structure for the data is similar to that of a stationary autoregressive process of order one. This paper investigates the effect that serially correlated training data have upon both the estimation of parameters and the classification problem. Results are included for both the Bayesian and maximum likelihood classification procedures.

Tubbs, J. D.↗
NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗