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Journal articles on the topic 'Class probability'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Młotkowski, Wojciech. "A class of probability measures." Journal of Computational and Applied Mathematics 133, no. 1-2 (2001): 694. http://dx.doi.org/10.1016/s0377-0427(00)00724-x.

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2

Lehnigk, S. H., and G. F. Roach. "On a class of probability distributions." Mathematical Methods in the Applied Sciences 9, no. 1 (1987): 210–19. http://dx.doi.org/10.1002/mma.1670090116.

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3

Simon, Richard. "Class probability estimation for medical studies." Biometrical Journal 56, no. 4 (2014): 597–600. http://dx.doi.org/10.1002/bimj.201300296.

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4

Kwok, J. T., I. W. H. Tsang, and J. M. Zurada. "A Class of Single-Class Minimax Probability Machines for Novelty Detection." IEEE Transactions on Neural Networks 18, no. 3 (2007): 778–85. http://dx.doi.org/10.1109/tnn.2007.891191.

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5

JIANG, LIANGXIAO, CHAOQUN LI, and ZHIHUA CAI. "DECISION TREE WITH BETTER CLASS PROBABILITY ESTIMATION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (2009): 745–63. http://dx.doi.org/10.1142/s0218001409007296.

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Traditionally, the performance of a classifier is measured by its classification accuracy or error rate. In fact, probability-based classifiers also produce the class probability estimation (the probability that a test instance belongs to the predicted class). This information is often ignored in classification, as long as the class with the highest class probability estimation is identical to the actual class. In many data mining applications, however, classification accuracy and error rate are not enough. For example, in direct marketing, we often need to deploy different promotion strategie

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6

Woon Jeung Park and Rhee Man Kil. "Pattern Classification With Class Probability Output Network." IEEE Transactions on Neural Networks 20, no. 10 (2009): 1659–73. http://dx.doi.org/10.1109/tnn.2009.2029103.

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7

Khoshyaran, Mahkame. "On a Class of Universal Probability Spaces." Advances in Research 6, no. 5 (2016): 1–7. http://dx.doi.org/10.9734/air/2016/24109.

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8

Wallace, Byron C., and Issa J. Dahabreh. "Improving class probability estimates for imbalanced data." Knowledge and Information Systems 41, no. 1 (2013): 33–52. http://dx.doi.org/10.1007/s10115-013-0670-6.

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9

Shahbaz, Saman, Muhammad Qaiser Shahbaz, M. Ahsanullah, and Muhammad Mohsin. "On a new class of probability distributions." Applied Mathematics Letters 24, no. 4 (2011): 545–52. http://dx.doi.org/10.1016/j.aml.2010.11.010.

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10

Kalkanis, G., and G. V. Conroy. "Interval error estimators in class probability trees." Pattern Recognition Letters 17, no. 7 (1996): 705–12. http://dx.doi.org/10.1016/0167-8655(96)00035-9.

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11

Haws, LaDawn. "Plinko, Probability, and Pascal." Mathematics Teacher 88, no. 4 (1995): 282–85. http://dx.doi.org/10.5951/mt.88.4.0282.

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In class, I recently referred to the television game show The Price Is Right and was surprised when nearly all the students broke into a grin of appreciation. As I thought about the show more seriously, I realized that many of the games played on the show contain an element of probability or problem solving. Analysis of some of the games is very simple and quite appropriate for an introductory probability class; others are a bit more complicated. This article discusses one of the most interesting games, Plinko.

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12

Nie, Qingfeng, Lizuo Jin, and Shumin Fei. "Probability estimation for multi-class classification using AdaBoost." Pattern Recognition 47, no. 12 (2014): 3931–40. http://dx.doi.org/10.1016/j.patcog.2014.06.008.

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13

XIN, Dong. "Probability output of multi-class support vector machines." Journal of Zhejiang University SCIENCE 3, no. 2 (2002): 131. http://dx.doi.org/10.1631/jzus.2002.0131.

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14

Ifantis, E. K., and C. G. Kokologiannaki. "Stieltjes transforms of a class of probability measures." Journal of Computational and Applied Mathematics 153, no. 1-2 (2003): 249–58. http://dx.doi.org/10.1016/s0377-0427(02)00616-7.

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15

Rhee, Robert J. "Probability, Policy and the Problem of Reference Class." International Journal of Evidence & Proof 11, no. 4 (2007): 286–91. http://dx.doi.org/10.1350/ijep.2007.11.4.286.

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16

Cramer, J. S. "ESTIMATION OF PROBABILITY MODELS FROM INCOME CLASS DATA." Statistica Neerlandica 40, no. 4 (1986): 237–50. http://dx.doi.org/10.1111/j.1467-9574.1986.tb01203.x.

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17

Lehnigk, Siegfried H. "Characteristic functions of a class of probability distributions." Complex Variables, Theory and Application: An International Journal 8, no. 3-4 (1987): 307–32. http://dx.doi.org/10.1080/17476938708814241.

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18

Sage, Andrew J., Ulrike Genschel, and Dan Nettleton. "Tree aggregation for random forest class probability estimation." Statistical Analysis and Data Mining: The ASA Data Science Journal 13, no. 2 (2020): 134–50. http://dx.doi.org/10.1002/sam.11446.

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19

Saar-Tsechansky, Maytal, and Foster Provost. "Active Sampling for Class Probability Estimation and Ranking." Machine Learning 54, no. 2 (2004): 153–78. http://dx.doi.org/10.1023/b:mach.0000011806.12374.c3.

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20

Xiong, Xiaoping. "A Class of Sequential Conditional Probability Ratio Tests." Journal of the American Statistical Association 90, no. 432 (1995): 1463–73. http://dx.doi.org/10.1080/01621459.1995.10476653.

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21

Clark, Stephen, and David Weir. "Class-Based Probability Estimation Using a Semantic Hierarchy." Computational Linguistics 28, no. 2 (2002): 187–206. http://dx.doi.org/10.1162/089120102760173643.

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This article concerns the estimation of a particular kind of probability, namely, the probability of a noun sense appearing as a particular argument of a predicate. In order to overcome the accompanying sparse-data problem, the proposal here is to define the probabilities in terms of senses from a semantic hierarchy and exploit the fact that the senses can be grouped into classes consisting of semantically similar senses. There is a particular focus on the problem of how to determine a suitable class for a given sense, or, alternatively, how to determine a suitable level of generalization in t

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22

Cao, Xi-Ren. "Realization probability in multi-class closed queuing networks." European Journal of Operational Research 36, no. 3 (1988): 393–401. http://dx.doi.org/10.1016/0377-2217(88)90132-4.

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23

Mohan, N. R., and S. Ravi. "On class , class , and compound distributions in reliability." Statistics & Probability Letters 61, no. 3 (2003): 269–76. http://dx.doi.org/10.1016/s0167-7152(02)00359-0.

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24

Okazaki, Hiroyuki. "Posterior Probability on Finite Set." Formalized Mathematics 20, no. 4 (2012): 257–63. http://dx.doi.org/10.2478/v10037-012-0030-0.

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Summary In [14] we formalized probability and probability distribution on a finite sample space. In this article first we propose a formalization of the class of finite sample spaces whose element’s probability distributions are equivalent with each other. Next, we formalize the probability measure of the class of sample spaces we have formalized above. Finally, we formalize the sampling and posterior probability.

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25

Smith, Ken. "Operationalizing Max Weber's probability concept of class situation: the concept of social class." British Journal of Sociology 58, no. 1 (2007): 87–104. http://dx.doi.org/10.1111/j.1468-4446.2007.00140.x.

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26

ZHDANOV, FEDOR, and YURI KALNISHKAN. "UNIVERSAL ALGORITHMS FOR PROBABILITY FORECASTING." International Journal on Artificial Intelligence Tools 21, no. 04 (2012): 1240015. http://dx.doi.org/10.1142/s0218213012400155.

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Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a linear model and by a kernelized model. The quality of predictions is measured by the Brier loss function. We obtain two computationally efficient algorithms for these problems by applying the Aggregating Algorithms to certain pools of experts and prove theoretical guarantees on the losses of these algorithms. We kernelize one of the algorithms and prove theoretical guarantees on its loss. We perform experiments and com

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27

Silveira, Fábio V. J., Frank Gomes-Silva, Cícero C. R. Brito, Moacyr Cunha-Filho, Felipe R. S. Gusmão, and Sílvio F. A. Xavier-Júnior. "Normal-G Class of Probability Distributions: Properties and Applications." Symmetry 11, no. 11 (2019): 1407. http://dx.doi.org/10.3390/sym11111407.

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In this paper, we propose a novel class of probability distributions called Normal-G. It has the advantage of demanding no additional parameters besides those of the parent distribution, thereby providing parsimonious models. Furthermore, the class enjoys the property of identifiability whenever the baseline is identifiable. We present special Normal-G sub-models, which can fit asymmetrical data with either positive or negative skew. Other important mathematical properties are described, such as the series expansion of the probability density function (pdf), which is used to derive expressions

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28

Tasche, Dirk. "Confidence Intervals for Class Prevalences under Prior Probability Shift." Machine Learning and Knowledge Extraction 1, no. 3 (2019): 805–31. http://dx.doi.org/10.3390/make1030047.

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Point estimation of class prevalences in the presence of dataset shift has been a popular research topic for more than two decades. Less attention has been paid to the construction of confidence and prediction intervals for estimates of class prevalences. One little considered question is whether or not it is necessary for practical purposes to distinguish confidence and prediction intervals. Another question so far not yet conclusively answered is whether or not the discriminatory power of the classifier or score at the basis of an estimation method matters for the accuracy of the estimates o

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29

Vásquez Ortiz, Claudia Alejandra, Ángel Alsina, Nataly Pincheira, María Magdalena Gea, and Eugenio Chandia. "Construction and validation of a probability class observation tool." Enseñanza de las Ciencias. Revista de investigación y experiencias didácticas 38, no. 2 (2020): 25. http://dx.doi.org/10.5565/rev/ensciencias.2820.

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30

Cadena, Meitner, Marie Kratz, and Edward Omey. "Characterization of a general class of tail probability distributions." Statistics & Probability Letters 154 (November 2019): 108553. http://dx.doi.org/10.1016/j.spl.2019.108553.

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31

Mao, Chang Xuan. "Predicting the Conditional Probability of Discovering a New Class." Journal of the American Statistical Association 99, no. 468 (2004): 1108–18. http://dx.doi.org/10.1198/016214504000001709.

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32

Jiang, Liangxiao, Zhihua Cai, Dianhong Wang, and Harry Zhang. "Improving Tree augmented Naive Bayes for class probability estimation." Knowledge-Based Systems 26 (February 2012): 239–45. http://dx.doi.org/10.1016/j.knosys.2011.08.010.

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33

Bae, Han Bin, Min Seop Park, Rhee Man Kil, and Hee Yong Youn. "Classifying heart conditions based on class probability output networks." Neurocomputing 360 (September 2019): 198–208. http://dx.doi.org/10.1016/j.neucom.2019.06.031.

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34

Gelman, Andrew, and Mark E. Glickman. "Some Class-Participation Demonstrations for Introductory Probability and Statistics." Journal of Educational and Behavioral Statistics 25, no. 1 (2000): 84–100. http://dx.doi.org/10.3102/10769986025001084.

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35

Gelman, Andrew, and Mark E. Glickman. "Some Class-Participation Demonstrations for Introductory Probability and Statistics." Journal of Educational and Behavioral Statistics 25, no. 1 (2000): 84. http://dx.doi.org/10.2307/1165313.

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36

Mallick, Avishek, and Indranil Ghosh. "A New Class of Mixture Probability Models with Applications." American Journal of Mathematical and Management Sciences 38, no. 2 (2018): 207–26. http://dx.doi.org/10.1080/01966324.2018.1507854.

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37

KANAMORI, T. "Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability." IEICE Transactions on Information and Systems E90-D, no. 12 (2007): 2033–42. http://dx.doi.org/10.1093/ietisy/e90-d.12.2033.

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38

Clifford, P., N. J. B. Green, J. F. Feng, and G. Wei. "Probability representations of a class of two-way diffusions." Journal of Physics A: Mathematical and General 35, no. 28 (2002): 5795–805. http://dx.doi.org/10.1088/0305-4470/35/28/301.

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39

Dombi, József, and Tamás Jónás. "Towards a general class of parametric probability weighting functions." Soft Computing 24, no. 21 (2020): 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.

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Abstract In this study, we present a novel methodology that can be used to generate parametric probability weighting functions, which play an important role in behavioral economics, by making use of the Dombi modifier operator of continuous-valued logic. Namely, we will show that the modifier operator satisfies the requirements for a probability weighting function. Next, we will demonstrate that the application of the modifier operator can be treated as a general approach to create parametric probability weighting functions including the most important ones such as the Prelec and the Ostaszews

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40

Marletto, Chiara. "Constructor theory of probability." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2192 (2016): 20150883. http://dx.doi.org/10.1098/rspa.2015.0883.

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Unitary quantum theory, having no Born Rule, is non-probabilistic . Hence the notorious problem of reconciling it with the unpredictability and appearance of stochasticity in quantum measurements. Generalizing and improving upon the so-called ‘decision-theoretic approach’, I shall recast that problem in the recently proposed constructor theory of information— where quantum theory is represented as one of a class of superinformation theories , which are local , non-probabilistic theories conforming to certain constructor-theoretic conditions. I prove that the unpredictability of measurement out

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41

HASEBE, TAKAHIRO. "FOURIER AND CAUCHY–STIELTJES TRANSFORMS OF POWER LAWS INCLUDING STABLE DISTRIBUTIONS." International Journal of Mathematics 23, no. 03 (2012): 1250041. http://dx.doi.org/10.1142/s0129167x12500413.

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We introduce a class of probability measures whose densities near infinity are mixtures of Pareto distributions. This class can be characterized by the Fourier transform which has a power series expansion including real powers, not only integer powers. This class includes stable distributions in probability and also noncommutative probability theories. We also characterize the class in terms of the Cauchy–Stieltjes transform and the Voiculescu transform. If the stability index is greater than one, stable distributions in probability theory do not belong to that class, while they do in noncommu

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42

BOSTRÖM, HENRIK. "FORESTS OF PROBABILITY ESTIMATION TREES." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 02 (2012): 1251001. http://dx.doi.org/10.1142/s0218001412510019.

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Probability estimation trees (PETs) generalize classification trees in that they assign class probability distributions instead of class labels to examples that are to be classified. This property has been demonstrated to allow PETs to outperform classification trees with respect to ranking performance, as measured by the area under the ROC curve (AUC). It has further been shown that the use of probability correction improves the performance of PETs. This has lead to the use of probability correction also in forests of PETs. However, it was recently observed that probability correction may in

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43

Müller, Alfred. "Integral Probability Metrics and Their Generating Classes of Functions." Advances in Applied Probability 29, no. 02 (1997): 429–43. http://dx.doi.org/10.1017/s000186780002807x.

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We consider probability metrics of the following type: for a class of functions and probability measures P, Q we define A unified study of such integral probability metrics is given. We characterize the maximal class of functions that generates such a metric. Further, we show how some interesting properties of these probability metrics arise directly from conditions on the generating class of functions. The results are illustrated by several examples, including the Kolmogorov metric, the Dudley metric and the stop-loss metric.

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44

Müller, Alfred. "Integral Probability Metrics and Their Generating Classes of Functions." Advances in Applied Probability 29, no. 2 (1997): 429–43. http://dx.doi.org/10.2307/1428011.

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We consider probability metrics of the following type: for a class of functions and probability measures P, Q we define A unified study of such integral probability metrics is given. We characterize the maximal class of functions that generates such a metric. Further, we show how some interesting properties of these probability metrics arise directly from conditions on the generating class of functions. The results are illustrated by several examples, including the Kolmogorov metric, the Dudley metric and the stop-loss metric.

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45

WANG, Lin, De-qin YAN, and Hong-xia LIANG. "Double coefficients support vector machine with probability and equivalence class." Journal of Computer Applications 29, no. 12 (2010): 3263–66. http://dx.doi.org/10.3724/sp.j.1087.2009.03263.

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46

Bell, James C., Robert L. Cunningham, and Matthew W. Havens. "Soil Drainage Class Probability Mapping Using a Soil-Landscape Model." Soil Science Society of America Journal 58, no. 2 (1994): 464–70. http://dx.doi.org/10.2136/sssaj1994.03615995005800020031x.

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47

Rongming, Wang, and Liu Haifeng. "On the Ruin Probability Under a Class of Risk Processes." ASTIN Bulletin 32, no. 1 (2002): 81–90. http://dx.doi.org/10.2143/ast.32.1.1016.

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AbstractIn this paper a class of risk processes in which claims occur as a renewal process is studied. A clear expression for Laplace transform of the finite time ruin probability is well given when the claim amount distribution is a mixed exponential. As its consequence, a well-known result about ultimate ruin probability in the classical risk model is obtained.

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48

Li, Jinjun. "A class of probability distribution functions preserving the packing dimension." Statistics & Probability Letters 81, no. 12 (2011): 1782–91. http://dx.doi.org/10.1016/j.spl.2011.07.010.

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49

Faridafshin, Farzad, and Arvid Naess. "Multivariate log-concave probability density class for structural reliability applications." Structural Safety 69 (November 2017): 57–67. http://dx.doi.org/10.1016/j.strusafe.2017.07.003.

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50

Costa, O. L. V., and F. Dufour. "Invariant probability measures for a class of Feller Markov chains." Statistics & Probability Letters 50, no. 1 (2000): 13–21. http://dx.doi.org/10.1016/s0167-7152(00)00075-4.

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