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Problém chybějících dat při sčítání lidu - kteří respondenti neodpověděli
Hora, Jan
The confidentiality of census data is known to be rather restrictive for economic and social research. To improve the availability of census information we have proposed recently a new method of interactive presentation of census results by means of statistical models. The method is based on estimation of the joint probability distribution of data records in the form of a distribution mixture. The estimated mixture model can be used as a knowledge base of a probabilistic expert system and in this way we can derive the statistical information from the distribution mixture without any further access to the original database. The statistical model does not contain the original data and therefore the final interactive software product can be made freely available via internet without any confidentiality concerns.
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Vyhodnocení stability jednotlivých metod i skupin metod výběru příznaků, který optimalizují kardinalitu podmnožiny příznaků
Somol, Petr ; Novovičová, Jana
Stability (robustness) of feature selection methods is a topic of recent interest yet often neglected importance with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown usable for assessing feature selection methods (or criteria) as such
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Informační shlukování kategoriálních dat
Hora, Jan
The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and, moreover, the estimated mixture parameters are starting-point dependent. For this reason we use the latent class model only to define a set of ``elementary'' classes by estimating a mixture of a large number components. As such a mixture we use also an optimally smoothed kernel estimate. We propose a hierarchical ``bottom up'' cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion.
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