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Author: search.filters.author.Naruedomkul, KanlayaSubject: search.filters.subject.Rough sets

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    Hybrid rough sets intelligent system architecture for survival analysis
    (2007-01-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    ;
    Naruedomkul, Kanlaya
    Survival analysis challenges researchers because of two issues. First, in practice, the studies do not span wide enough to collect all survival times of each individual patient. All of these patients require censor variables and cannot be analyzed without special treatment. Second, analyzing risk factors to indicate the significance of the effect on survival time is necessary. Hence, we propose "Enhanced Hybrid Rough Sets Intelligent System Architecture for Survival Analysis" (Enhanced HYRIS) that can circumvent these two extra issues. Given the survival data set, Enhanced HYRIS can analyze and construct a life time table and Kaplan-Meier survival curves that account for censor variables. We employ three statistical hypothesis tests and use the p-value to identify the significance of a particular risk factor. Subsequently, rough set theory generates the probe reducts and reducts. Probe reducts and reducts include only a risk factor subset that is large enough to include all of the essential information and small enough for our survival prediction model to be created. Furthermore, in the rule induction stage we offer survival prediction models in the form of decision rules and association rules. In the validation stage, we provide cross validation with ELEM2 as well as decision tree. To demonstrate the utility of our methods, we apply Enhanced HYRIS to various data sets: geriatric, melanoma and primary biliary cirrhosis (PBC) data sets. Our experiments cover analyzing risk factors, performing hypothesis tests and we induce survival prediction models that can predict survival time efficiently and accurately. © Springer-Verlag Berlin Heidelberg 2007.
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    Rule analysis with rough sets theory
    (2006-11-22)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    ;
    Naruedomkul, Kanlaya
    Postprocessing is a significant step in the data analysis process which is often ignored or glossed over. Once we have a large set of generated rules, how can we elicit the sufficient and necessary rules? In this paper, we propose an alternative approach for decision rule learning with rough sets theory in the postprocessing step called 'ROSERULE'. Essentially, we introduce rule reducts, a sufficient and necessary part which preserves classification of the rule universe, as a rough sets tool for rule analysis. ROSERULE learns and analyzes from the rule set to generate rule reducts which can be used to reduce the number of the rules. This is in contrast to common rule analysis which simply performs rule selection. We illustrate the performance of ROSERULE with several case studies; melanoma, primary biliary cirrhosis, pneumonia and a real-world case study, geriatric data sets. ROSERULE is run on these data sets and the result are a reduced number of rules that successfully preserve the original classification. © 2006 IEEE.