Search NASA⌕ Search

SEARCH · Search NASA

Results for “Misclassification”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Sufficient Statistics for Divergence and the Probability of Misclassification

One particular aspect is considered of the feature selection problem which results from the transformation x=Bz, where B is a k by n matrix of rank k and k is or = to n. It is shown that in general, such a transformation results in a loss of information. In terms of the divergence, this is equivalent to the fact that the average divergence computed using the variable x is less than or equal to the average divergence computed using the variable z. A loss of information in terms of the probability of misclassification is shown to be equivalent to the fact that the probability of misclassification computed using variable x is greater than or equal to the probability of misclassification computed using variable z. First, the necessary facts relating k-dimensional and n-dimensional integrals are derived. Then the mentioned results about the divergence and probability of misclassification are derived. Finally it is shown that if no information is lost (in x = Bz) as measured by the divergence, then no information is lost as measured by the probability of misclassification.

Quirein, J.↗
NASA NTRS1973

On minimizing the probability of misclassification for linear feature selection

The use of techniques for feature selection permits treatment of classification problems in spaces of reduced dimensions. A method is considered of linear feature selection for n-dimensional observation vectors which belong to one of two populations, where each population is described by a known multivariate normal density function. More specifically, the problem of finding a 1xn transformation matrix B for which the probability of misclassification with respect to the one-dimensional transformed density functions was minimized was considered. Theoretical results are presented which give rise to a numerically tractable expression for the variation in the probability of misclassification with respect to B. Using this expression a computational procedure is discussed for obtaining a B which minimizes the probability of misclassification. Preliminary numerical results are discussed.

Guseman, L. F., Jr.↗
NASA NTRS1972

Rank-k Maximal Statistics for Divergence and Probability of Misclassification

A technique is developed for selecting from n-channel multispectral data some k combinations of the n-channels upon which to base a given classification technique so that some measure of the loss of the ability to distinguish between classes, using the compressed k-dimensional data, is minimized. Information loss in compressing the n-channel data to k channels is taken to be the difference in the average interclass divergences (or probability of misclassification) in n-space and in k-space.

Decell, H. P., Jr.↗
NASA NTRS1975

LFSPMC: Linear feature selection program using the probability of misclassification

The computational procedure and associated computer program for a linear feature selection technique are presented. The technique assumes that: a finite number, m, of classes exists; each class is described by an n-dimensional multivariate normal density function of its measurement vectors; the mean vector and covariance matrix for each density function are known (or can be estimated); and the a priori probability for each class is known. The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.

Guseman, L. F., Jr.↗
NASA NTRS1994

Pigeonholing planetary meteorites: The lessons of misclassification of EET87521 and ALH84001

The last few years have provided two noteworthy examples of misclassifications of achondritic meteorites because the samples were new kinds of meteorites from planetary rather than asteroidal parent bodies. Basaltic lunar meteorite EET87521 was misclassified as a eucrite and SNC (martian) orthopyroxenite ALH84001 was misclassified as a diogenite. In classifying meteorites we find what we expect: we pigeonhole meteorites into known categories most of which were derived from the more common asteroidal meteorites. But the examples of EET8752 and ALH84001 remind us that planets are more complex than asteroids and exhibit a wider variety of rock types. We should expect variety in planetary meteorites and we need to know how to recognize them when we have them. Our intent here is to show that our asteroidal perspective is inappropriate for planetary meteorites.

Lindstrom, M. M.↗
NASA NTRS1990

Experimental fault characterization of a neural network

The effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size.

Tan, Chang-Huong↗
NASA NTRS1975

Study of recreational land and open space using Skylab imagery

The author has identified the following significant results. An analysis of the statistical uniqueness of each of the signatures of the Gratiot-Saginaw State Game Area was made by computing a matrix of probabilities of misclassification for all possible signature pairs. Within each data set, the 35 signatures were then aggregated into a smaller set of composite signatures by combining groups of signatures having high probabilities of misclassification. Computer separation of forest denisty classes was poor with multispectral scanner data collected on 5 August 1973. Signatures from the scanner data were further analyzed to determine the ranking of spectral channels for computer separation of the scene classes. Probabilities of misclassification were computed for composite signatures using four separate combinations of data source and channel selection.

Sattinger, I. J.↗
NASA NTRS1973

Sufficient Statistics: an Example

The feature selection problem is considered resulting from the transformation x = Bz where B is a k by n matrix of rank k and k is or = to n. Such a transformation can be considered to reduce the dimension of each observation vector z, and in general, such a transformation results in a loss of information. In terms of the divergence, this information loss is expressed by the fact that the average divergence D sub B computed using variable x is less than or equal to the average divergence D computed using variable z. If D sub B = D, then B is said to be a sufficient statistic for the average divergence D. If B is a sufficient statistic for the average divergence, then it can be shown that the probability of misclassification computed using variable x (of dimension k is or = to n) is equal to the probability of misclassification computed using variable z. Also included is what is believed to be a new proof of the well known fact that D is or = to D sub B. Using the techniques necessary to prove the above fact, it is shown that the Brattacharyya distance as measured by variable x is less than or equal to the Brattacharyya distance as measured by variable z.

Quirein, J.↗
NASA NTRS1974

On differentiating the probability of error in multipopular feature selection

A method of linear feature selection for n dimensional observation vectors which belong to one of m populations is presented. Each population has a known apriori probability and is described by a known multivariate normal density function. Specifically we consider the problem of finding a k x n matrix B of rank k (k n) for which the transformed probability of misclassification is minimized. Providing that the transformed a posterior probabilities are distinct theoretical results are obtained which, for the case k = l, give rise to a numerically tractable formula for the derivative of the probability of misclassification. It is shown that for the two population problem this condition is also necessary. The dependence of the minimum probability of error on the a priori probabilities is investigated. The minimum probability of error satisfies a uniform Lipschitz condition with respect to the a priori probabilities.

Peters, B. C.↗
NASA NTRS1974

Use of a diffuser for producing texture masks for image processing

The use of a diffuser in synthesizing binary texture-variance masks for applications in image processing is investigated. The binary texture-variance masks are used to separate various objects in an image on the basis of their spatial texture variance. The diffuser is required to reduce the speckling present in the mask. The reduction in speckling is described quantitatively as a reduction of the misclassification probability associated with the separation of the objects into several texture classes. General results are furnished for the statistics of the field scattered by the diffuser, and these are used to obtain expressions for the misclassification probabilities in the presence of a diffuser.

Stark, H.↗
NASA NTRS1975

Study of recreational land and open space using Skylab imagery

The author has identified the following significant results. A preliminary analysis of the Gratiot-Saginaw game area was conducted for the matrix probabilities of misclassification for both multispectral scanner signatures and the ERTS signatures. This analysis indicated that certain signatures of similar vegetation types had relatively high probabilities of misclassification and should logically be combined into a single signature for each major class. This would result in a single class of trees having a broad range of crown densities and two or three types of wetlands. It was clear that substantially different terrain classes could be reliably discriminated. It was also noted that areas of pine and regenerated aspen could be discriminated from other surface types.

Sattinger, I. J.↗
NASA NTRS1975

An algorithm for optimal single linear feature extraction from several Gaussian pattern classes

A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. Numerical results on the application of this procedure to the remotely sensed data from the Purdue Cl flight line as well as LANDSAT data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. Also, the optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence.

Starks, S. A.↗
NASA NTRS2001

Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP)

The relative effectiveness of average-well-color-development-normalized single-point absorbance readings (AWCD) vs the kinetic parameters mu(m), lambda, A, and integral (AREA) of the modified Gompertz equation fit to the color development curve resulting from reduction of a redox sensitive dye from microbial respiration of 95 separate sole carbon sources in microplate wells was compared for a dilution series of rhizosphere samples from hydroponically grown wheat and potato ranging in inoculum densities of 1 x 10(4)-4 x 10(6) cells ml-1. Patterns generated with each parameter were analyzed using principal component analysis (PCA) and discriminant function analysis (DFA) to test relative resolving power. Samples of equivalent cell density (undiluted samples) were correctly classified by rhizosphere type for all parameters based on DFA analysis of the first five PC scores. Analysis of undiluted and 1:4 diluted samples resulted in misclassification of at least two of the wheat samples for all parameters except the AWCD normalized (0.50 abs. units) data, and analysis of undiluted, 1:4, and 1:16 diluted samples resulted in misclassification for all parameter types. Ordination of samples along the first principal component (PC) was correlated to inoculum density in analyses performed on all of the kinetic parameters, but no such influence was seen for AWCD-derived results. The carbon sources responsible for classification differed among the variable types with the exception of AREA and A, which were strongly correlated. These results indicate that the use of kinetic parameters for pattern analysis in CLPP may provide some additional information, but only if the influence of inoculum density is carefully considered. c2001 Elsevier Science Ltd. All rights reserved.

NASA Center KSC↗
NASA NTRS2006

Less-Complex Method of Classifying MPSK

An alternative to an optimal method of automated classification of signals modulated with M-ary phase-shift-keying (M-ary PSK or MPSK) has been derived. The alternative method is approximate, but it offers nearly optimal performance and entails much less complexity, which translates to much less computation time. Modulation classification is becoming increasingly important in radio-communication systems that utilize multiple data modulation schemes and include software-defined or software-controlled receivers. Such a receiver may "know" little a priori about an incoming signal but may be required to correctly classify its data rate, modulation type, and forward error-correction code before properly configuring itself to acquire and track the symbol timing, carrier frequency, and phase, and ultimately produce decoded bits. Modulation classification has long been an important component of military interception of initially unknown radio signals transmitted by adversaries. Modulation classification may also be useful for enabling cellular telephones to automatically recognize different signal types and configure themselves accordingly. The concept of modulation classification as outlined in the preceding paragraph is quite general. However, at the present early stage of development, and for the purpose of describing the present alternative method, the term "modulation classification" or simply "classification" signifies, more specifically, a distinction between M-ary and M'-ary PSK, where M and M' represent two different integer multiples of 2. Both the prior optimal method and the present alternative method require the acquisition of magnitude and phase values of a number (N) of consecutive baseband samples of the incoming signal + noise. The prior optimal method is based on a maximum- likelihood (ML) classification rule that requires a calculation of likelihood functions for the M and M' hypotheses: Each likelihood function is an integral, over a full cycle of carrier phase, of a complicated sum of functions of the baseband sample values, the carrier phase, the carrier-signal and noise magnitudes, and M or M'. Then the likelihood ratio, defined as the ratio between the likelihood functions, is computed, leading to the choice of whichever hypothesis - M or M'- is more likely. In the alternative method, the integral in each likelihood function is approximated by a sum over values of the integrand sampled at a number, 1, of equally spaced values of carrier phase. Used in this way, 1 is a parameter that can be adjusted to trade computational complexity against the probability of misclassification. In the limit as 1 approaches infinity, one obtains the integral form of the likelihood function and thus recovers the ML classification. The present approximate method has been tested in comparison with the ML method by means of computational simulations. The results of the simulations have shown that the performance (as quantified by probability of misclassification) of the approximate method is nearly indistinguishable from that of the ML method (see figure).

Hamkins, Jon↗
NASA NTRS2018

Reply to "Comments on 'A CloudSat-CALIPSO View of Cloud and Precipitation Properties Across Cold Fronts over the Global Oceans'"

In Naud et al., a compositing method was utilized with CloudSat-CALIPSO observations to obtain mean transects of cloud vertical distribution and surface precipitation across cold fronts, and to examine their sensitivity to the large-scale properties of the parent extratropical cyclone. This reply demonstrates the value of compositing for evaluating numerical models, and presents additional results that address the issue of the sensitivity of the initial results to the frontal detection methodology and the potential misclassification of occlusions as cold fronts. Here a sensitivity study of the cold front composite transects of cloud cover to the input datasets or the method utilized to locate the cold fronts demonstrates that these composite transects are robust and only marginally sensitive to cold front location methods. The same conclusion is reached for the robustness of the contrast between Northern and Southern Hemisphere cloud transects. While occlusions cannot directly be flagged within the database at this point, comparisons of transects obtained for subsets of cyclones of different age indicate that the misclassification of occluded fronts as cold fronts does not explain the predominance of cloud and precipitation on the warm side of the cold fronts. The strong signal on the warm side might be better explained by a predominance of forward sloping cold fronts, or the presence of the warm conveyor belt.

Cloud cover; Cold fronts; Extratropical cyclones; ↗
NASA NTRS2018

(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

Lamer, K.↗