Multiple controlled random testing

Рассматриваются различные способы формирования управляемых вероятностных тестов и их недостатки. Предлагаются и анализируются различные численные метрики для построения многократных управляемых вероятностных тестов. Обосновывается и подтверждается экспериментальными исследованиями эффективность применения расстояния Евклида для формирования многократных управляемых вероятностных тестов. Various methods for generating controlled random tests and their disadvantages are considered. Various numerical metrics are proposed and analyzed to construct multiple controlled random tests. The effectiveness of applying the Euclidean distance for the formation of multiple controlled random tests is substantiated and confirmed by experimental studies

Generowanie wieloprzebiegowych kontrolowanych testów losowych

Controlled Random Tests and methods for their generation have been analyzed and investigated. The similarities of all known controlled random testing approaches are shown. The new method and algorithm for Multiple Controlled Random Tests have been proposed and analyzed

Basic studies in microwave remote sensing

Scattering models were developed in support of microwave remote sensing of earth terrains with particular emphasis on model applications to airborne Synthetic Aperture Radar measurements of forest. Practically useful surface scattering models based on a solution of a pair of integral equations including multiple scattering effects were developed. Comparisons of these models with controlled scattering measurements from statistically known random surfaces indicate that they are valid over a wide range of frequencies. Scattering models treating a forest environment as a two and three layered media were also developed. Extensive testing and comparisons were carried out with the two layered model. Further studies with the three layered model are being carried out. A volume scattering model valid for dense media such as a snow layer was also developed that shows the appropriate trend dependence with the volume fraction of scatterers

МНОГОКРАТНЫЕ УПРАВЛЯЕМЫЕ ВЕРОЯТНОСТНЫЕ ТЕСТЫ

Controlled Random Tests and methods for their generation have been analyzed and investigated. The similarities of all known controlled random testing approaches are shown. A new method and algorithm for Multiple Controlled Random Tests have been proposed and analyzed.Рассматриваются однократные управляемые вероятностные тесты, методы их формирования, а также их применение для тестирования средств вычислительных систем. Показываются основные недостатки построения однократных вероятностных тестов. Предлагается метод построения многократных управляемых вероятностных тестов на базе исходного однократного теста. Анализируются различные численные метрики для построения как однократных, так и многократных управляемых вероятностных тестов

Methods of synthesis of controlled random tests

Controlled random tests, methods of their generation, main criteria used for their synthesis, such as the Hamming distance and the Euclidean distance, as well as their application to the testing of both hardware and software systems are discussed. Available evidences suggest that high computational complexity is one of the main drawbacks of these methods. Therefore we propose a technique to overcome this problem. A method for synthesizing multiple controlled random tests based on the use of the initial random test and addition operation has been proposed. The resulting multiple tests can be interpreted as a single controlled random test. The complexity of its construction is significantly lower than the complexity of the construction of classical random tests. Examples of generated tests as well as estimates of their effectiveness compared to other solutions have been presented in experimental studies

Multiple Hypothesis Testing in Pattern Discovery

The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entails a host of hypothesis, one for each itemset. A multiple hypothesis testing method is needed to control the number of false positives (Type I error). Our contribution in this paper is to extend the multiple hypothesis framework to be used with a generic data mining algorithm. We provide a method that provably controls the family-wise error rate (FWER, the probability of at least one false positive) in the strong sense. We evaluate the performance of our solution on both real and generated data. The results show that our method controls the FWER while maintaining the power of the test.Comment: 28 page