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Topic: Binary classification

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	Binary Star - MSN Encarta
		Binary Star, two stars that are bound to each other by gravity and orbit about a common center of mass.
		Some close binary pairs—for example the W Ursa Majoris system—are composed of otherwise ordinary stars that are so close that their mutual gravitational pull distorts the shape of one or both stars from a spherical shape into an egg shape.
		In an interacting binary system consisting of a normal star and a collapsed star—for example a white dwarf, a neutron star, or a fl hole—the gravitational field at the surface of the collapsed star is so strong that the flow of material onto its surface causes bursts of highly energetic X rays.
	encarta.msn.com /encyclopedia_761586362/Binary_Star.html (1296 words)

	Binary classification - Wikipedia, the free encyclopedia
		Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not.
		Classification in general is one of the problems studied in computer science, in order to automatically learn classification systems; some methods suitable for learning binary classifiers include the decision trees, Bayesian networks, support vector machines, and neural networks.
		In addition to sensitivity and specificity, the performance of a binary classification test can be measured with positive and negative predictive values.
	en.wikipedia.org /wiki/Binary_classification (1006 words)

	Binary classification - Definition, explanation
		Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not.
		Classification in general is one of the problems studied in computer science, in order to automatically learn classification systems; some methods suitable for learning binary classifiers include the decision trees, Bayesian networks, support vector machines, and neural networks.
		To measure the performance of a medical test, the concepts sensitivity and specificity are often used; these concepts are readily usable for the evaluation of any binary classifier.
	www.calsky.com /lexikon/en/txt/b/bi/binary_classification.php (985 words)

	Encyclopedia :: encyclopedia : Scientific classification (Site not responding. Last check: 2007-10-20)
		Scientific classification or biological classification is how biologists group and categorize extinct and living species of organisms.
		Advances in classification due to the work of entomologists and the first microscopists is due to the research of people like Marcello Malpighi (1628–1694), Jan Swammerdam (1637–1680), and Robert Hooke (1635–1702).
		The usual classifications of five species follow: the fruit fly so familiar in genetics laboratories (Drosophila melanogaster), humans (Homo sapiens), the peas used by Gregor Mendel in his discovery of genetics (Pisum sativum), the fly agaric mushroom Amanita muscaria, and the bacterium Escherichia coli.
	www.hallencyclopedia.com /Scientific_classification (1028 words)

	Classification Trees
		Classification trees are used to predict membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables.
		The study and use of classification trees are not widespread in the fields of probability and statistical pattern recognition (Ripley, 1996), but classification trees are widely used in applied fields as diverse as medicine (diagnosis), computer science (data structures), botany (classification), and psychology (decision theory).
		The second basic step in classification tree analysis is to select the splits on the predictor variables which are used to predict membership in the classes of the dependent variables for the cases or objects in the analysis.
	www.statsoft.com /textbook/stclatre.html (8271 words)

	untitled
		Classifications are used in everyday life and science.
		The labels are the titles of the different classes in the classification system; the rules are the tests you apply to decide in which class to place an object.
		The binary classification system groups objects on the basis of whether or not they possess a certain property.
	ivc.uidaho.edu /integrated/assignments/classification.html (643 words)

	Classification of Binary Document Images into Texture or Non-textual Data Blocks Using Neural Network Models
		A binary image refers to a two-dimensional binary function f (i, j), whose value is either 0 or 1, where i and j denote the vertical and horizontal coordinates of the pixel, respectively, and where the coordinates' origin is located at the top left corner of the image.
		As pointed out by Ng and Lippmann(10), the classification accuracy of the RBF classifier depends upon both H and F. The factor H is included to provide flexibility in setting the width of the basis function.
		Table 2 presents the classification accuracy on the testing data set for all values of F and H. Table 2 shows that the classification accuracy of the trained RBF on the testing data set is about 99.61 % for F = 10 and H = 150.
	archive.nlm.nih.gov /pubs/doc_class/mv.php (6760 words)

	BioMed Central \| Full text \| Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a ...
		The classification performance of the overall(both genders) models which included Gender as a predictor is summarized in Table 3.
		Classification performance for models fitted separately to each gender is shown in Table 4.
		At least for binary prediction of dyslipidemia from waist-to-hip ratio and body mass index in the context of the relatively small set of other predictor variables examined, the simple additive logistic models obtained in previous studies were about as effective as the more comprehensive statistical models investigated here.
	www.biomedcentral.com /1471-2288/4/7 (4174 words)

	Implement Bayesian inference using PHP: Part 3
		The final paragraph of this quote is interesting because it suggests that the goal of data analysis in classifier-oriented research should be to construct a classifier that is, first of all, accurate and, after that, one that is the simplest in terms of the number of variables that are used by the classifier.
		The classification question was whether the patient had a positive or negative coronary disease test (defined as coronary artery stenosis of at least 70 percent).
		In general, as you pass from simple classification tasks to multivariate classification tasks, the most difficult and complex issue you have to deal with is whether your object attributes are independent or not.
	www-128.ibm.com /developerworks/web/library/wa-bayes3 (4034 words)

	2001 Annual Meeting Information
		Sex and gender classification systems, on the other hand, just started to become the subject of litigation in the last half of the twentieth century and it is only during the last decade that sex classification systems have become the topic of extensive scholarly comment.
		Sex and racial classifications are clearly not completely analogous because of the strong biological basis to sex determination, the ability of sexual minorities to hide their sexual identity and, most importantly, our history of slavery which so profoundly shaped race relations in this country.
		Sex classification systems, on the other hand, are still based on the assumptions that sex is binary, unambiguous, and can be biologically determined, despite scientific research that indicates that none of these assumptions are completely accurate.
	www.aals.org /am2001/mat_greenberg.html (4331 words)

	Internet-Based Basin Boundary Visualization for 3D Dynamical Systems
		For some applications, a binary subdivision of the volume is not sufficient, for instance for the representation of basin boundaries in the phase space of dynamical systems.
		Binary subdivision for refinement of regions leads to a coarse representation of the surface, therefore the vertices of the triangles are relocated after the surface
		These binary cells contain only vertices with identical and adjacent classifications from the general cell, the remaining vertices are assigned to a so-called not defined region.
	www.cg.tuwien.ac.at /studentwork/CESCG/CESCG-2000/BWallisch (4452 words)

	The Gaussian Processes Web Site
		Binary classification is implemented using both the Laplace approximation and the Expectation Propagation algorithm.
		Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling.
		Existing bounds (Jaakkola and Jordan 1996) for the sigmoid function are used to tackle binary problems and multi-dimensional bounds on the softmax function are presented for the multiple class case.
	www.gaussianprocess.org (12048 words)

	Directory of open access journals
		Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models
		We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements.
		Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region.
	www.doaj.org /abstract?id=83955&toc=y (273 words)

	Paul Komarek's Logistic Regression Page
		Logistic regression (LR) is a statistical model suitable for probabilistic binary classification, though it is not limited to this use.
		The LR model represents the probability that a data point belongs to the "positive" class (an arbitrary label for one of the two classes in the context of binary classification).
		The binary classifier is run once for each output class, and the probabilities recorded for each dataset record.
	komarix.org /ac/lr (2690 words)

	Untitled Document (Site not responding. Last check: 2007-10-20)
		However, it is nontrivial to extend the binary margin-based algorithms to multi-class cases, because the current formulation of the margin-based classifier is specifically designed for the binary classification problem.
		A widely used strategy for solving the multi-class classification problem is to employ the one-versus-all method such that a K-class problem is reduced to K binary classification problems.
		We define the margin vector which is the multi-class generalization of the margin, then we further generalize the concept of admissible loss in binary classification to the multi-class cases.
	www.biostat.umn.edu /seminar/zou120705.htm (196 words)

	Classification and Regression Trees
		In the case of a categorical response (classification problem), minimizing costs amounts to minimizing the proportion of misclassified cases when priors are taken to be proportional to the class sizes and when misclassification costs are taken to be equal for every class.
		The second basic step in classification and regression trees is to select the splits on the predictor variables that are used to predict membership in classes of the categorical dependent variables, or to predict values of the continuous dependent (response) variable.
		For classification problems, if the priors used in the analysis are equal and class sizes are equal as well, then splitting will stop when all terminal nodes containing more than one class have no more cases than the specified fraction of the class sizes for one or more classes.
	www.statsoft.com /textbook/stcart.html (5040 words)

	Classifying and Sequencing
		In a binary classification system, a set of objects is divided into two subsets on the basis of whether each object has or does not have a particular property.
		To construct a binary classification system, you must first identify a property which some of the objects have but that none of the other objects have.
		There are three parts to this exercise: (A) identifying characteristics of a binary system of classification, (B) constructing and using a dichotomous classification system, and (C) serial ordering.
	www.accessexcellence.org /AE/ATG/data/released/0182-JimMariner (412 words)

	SPEC Glossary
		To be specific, binary refers to a numeric representation that is comprised of (frequently very long) sequences of only two values, usually '0' and '1'.
		For the purposes of classification for the CPU benchmarks, SPEC classifies an application to be a floating point application, if that application typically spends 10% or more of its time in calculating floating point values.
		It is usually a binary representation of the program statements translated into a form that is understood natively by the processor.
	www.spec.org /spec/glossary (3014 words)

	[No title]
		Decomposing a K-class classification problem into a number of binary classification problems allows an ensemble scheme to model binary class boundaries with much greater flexibility at a lower computational cost [Goh01].
		ECOC was first proposed by Dietterich and Bakiri [S] to reduce classification error by exploiting the redundancy of the coding scheme.
		ECOC employs a set of binary classifiers assigned with codewords such that the Hamming distance between each pair is far enough apart to enable good error correction.
	www.it.iitb.ac.in /~rvijay/phd/it642/project/svm-binary.doc (1467 words)

	Tech Report: HPL-2005-179: A Statistical Combined Classifier
		Reported here are metrics for distinguishing photos and color drawings, and a novel classification technique based solely on the statistics of each heuristic metric.
		The statistical technique allows ready combination of multiple binary classifiers, and provides a lower classification error than simple voting or metric-confidence techniques.
		We herein present a method for binary classification that accommodates any number of individual classifiers.
	www.hpl.hp.com /techreports/2005/HPL-2005-179.html?mtxs=rss-hpl-tr (318 words)

	Binary Classification Example
		The third and fourth nominal attributes have three and four classifications respectively.
		All four attributes are encoded using binary encoding.
		Since the target output is binary classification the logistic activation function is used in the output layer.
	www.vni.com /products/imsl/jmsl/v40/api/com/imsl/datamining/neural/BinaryClassificationEx1.html (537 words)

	Interval estimation of the kappa coefficient with binary classification and an equal marginal probability model.
		Interval estimation of the kappa coefficient with binary classification and an equal marginal probability model.
		We derive a likelihood score method for interval estimation of the intraclass version of the kappa coefficient of agreement with binary classification using a general theory of Bartlett (1953, Biometrika 40, 306-317).
		By exact evaluation, we investigate statistical properties of the score method, the chi-square goodness-of-fit procedure (Donner and Eliasziw, 1992, Statistics in Medicine 11, 1511-1519; Hale and Fleiss, 1993, Biometrics 49, 523-534), and a crude confidence interval for small and medium sample sizes.
	www.aegis.com /aidsline/2000/dec/A00C0495.html (381 words)

	An Empirical Study of Optimism and Selection Bias in Binary Classification with Microarray Data
		Motivation: Feature subset selection is a very important aspect of performing binary classification using gene expression data.
		For five of the six datasets, the misclassification rates and bias estimates were very consistent, suggesting that these results should generalize well to other clinical microarray datasets.
		Michael L. Lecocke and Kenneth Hess, "An Empirical Study of Optimism and Selection Bias in Binary Classification with Microarray Data" (December 2004).
	www.bepress.com /mdandersonbiostat/paper3 (413 words)

	Event \| Adaptive Linear Kernels for Binary Classification of Multiclass Data: Application to Speaker Recognition (Site not responding. Last check: 2007-10-20)
		More generally, I'll be discussing kernel selection and optimization for tasks involving binary classification of multiclass data---for example, one-versus-all (OVA) classification tasks.
		The talk will focus on two related techniques that use first and second-order statistics from each class to construct an upper bound on false positives and false negatives in a linear classifier.
		In experiments on various speaker recognition tasks, this modified SVM formulation yields substantial improvements in classification accuracy over our baseline SVM system, where the features are scaled to have unit variance in the training set.
	www.icsi.berkeley.edu /cgi-bin/events/event.pl?ID=000341 (220 words)

	Cross-Validation for Binary Classification By Real-Valued Functions: Theoretical Analysis - Anthony, Holden ...
		If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
		Previous work in this area has concentrated on using as an error estimate the `resubstitution' error (that is, the empirical error of a classi er on the training sample) or its derivatives.
		Cross-validation for binary classification by real-valued functions: Theoretical analysis.
	citeseer.ist.psu.edu /anthony98crossvalidation.html (584 words)

	Evaluation of SVM Binary Classification with Nonparametric Stochastic Simulations (Site not responding. Last check: 2007-10-20)
		The quality of Support Vector Machines binary classification of spatial environmental data is evaluated with geostatistical nonparametrtic conditional stochastic simulations.
		Case study is based on the classification of porosity data.
		Results obtained confirm the efficiency of the SVM binary classification of spatial data
	www.idiap.ch /publications/RR-01-07.bib.abs.html (84 words)

	Citebase - The Signed Distance Function: A New Tool for Binary Classification
		Authors: Boczko, Erik M. Young, Todd R. From a geometric perspective most nonlinear binary classification algorithms, including state of the art versions of Support Vector Machine (SVM) and Radial Basis Function Network (RBFN) classifiers, and are based on the idea of reconstructing indicator functions.
		We propose instead to use reconstruction of the signed distance function (SDF) as a basis for binary classification.
		Performance of the new methods also matches or exceeds that of standard methods on several nonlinear problems including classification of benchmark diagnostic micro-array data sets.
	citebase.eprints.org /cgi-bin/citations?id=oai:arXiv.org:cs/0511105 (832 words)

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