Journal ArticleDOI
Determination of sample size using power analysis and optimum bin size of histogram features
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This paper provides a mathematical study to choose the bin size and the minimum sample size to train the classifier using power analysis with statistical stability and the results are compared with that of entropy based algorithm (J48) for determiningminimum sample size and bin size.Abstract:
Vibration signals are used in fault diagnosis of rotary machines as a source of information. Lots of work have been reported on identification of faults in roller bearing by using many techniques. Of late, application of machine learning approach in fault diagnosis is gaining momentum. Machine learning approach consists of chain of activities like, data acquisition, feature extraction, feature selection and feature classification. While histogram features are used, there are still a few questions to be answered such as how many histogram bins are to be used to extract features and how many samples to be used to train the classifier. This paper provides a mathematical study to choose the bin size and the minimum sample size to train the classifier using power analysis with statistical stability. A typical bearing fault diagnosis problem is taken as a case for illustration and the results are compared with that of entropy based algorithm (J48) for determining minimum sample size and bin size.read more
Citations
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A confidence-prioritisation approach for learning noisy data
TL;DR: This work proposes a methodological framework for assigning confidence to individual data records and augmenting training with that information, and results indicate that applying and utilising confidence in training improves performance.
References
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Journal ArticleDOI
On the sample size for studies based upon McNemar's test.
TL;DR: The sample size needed in many studies using McNemar's test, where the investigator is unable to specify the probability of discordance, but can state, at least approximately, the marginal probabilities of each variable, is computed.
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A graphical aid for determining sample size when comparing two independent proportions.
TL;DR: Standard sample size calculations for n, the number of observations per group when comparing two independent proportions, P1 and P2, require the specification of four quantities: P1, one of the two proportions of interest; delta, the smallest difference which it is important to detect; alpha, the significance level; and beta, the chance of failing to detect a difference as large as delta.
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Sample size determination for case-control studies and the comparison of stratified and unstratified analyses
TL;DR: A sample size formula is derived for Cochran's statistic with continuity correction which guarantees that the actual Type I error rate of the test does not exceed the nominal level and is useful in the design of case-control studies.
Journal ArticleDOI
Sample size requirements for studies estimating odds ratios or relative risks
TL;DR: These formulae provide guidelines for determination of study size that does not depend on hypothesis testing considerations and differs somewhat from previous comparable work that estimated the log odds ratio within a stated fixed distance rather than as a percentage of the actual odds ratio.
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On the Moments of the Trace of a Matrix and Approximations to its Distribution
K. C. S. Pillai,Tito A. Mijares +1 more
TL;DR: In this article, the first four moments of the sum of six non-null latent roots of a matrix occurring in multivariate analysis were studied, and the upper percentage points obtained directly from the moment ratios with those from Pillai's approximate distribution were compared.