The Feature Selection Effect on Missing Value Imputation of Medical Datasets
نویسندگان
چکیده
منابع مشابه
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چکیده این تحقیق " مواد درسی " را به عنوان یکی از بحث برانگیزترین موضوعات آموزش زبان تلقی کرده و درتلاش است به سه سوال عمده پاسخ دهد: 1) آیا بکارگیری جزوات گرداوری شده توسط گروهی از مدرسین تاثیر بسزایی در میزان توانش خواندن و درک متون انگلیسی دانشجویان پزشکی دارد؟ 2) آیا استفاده از مواد درسی اصلی( بین المللی )، توانش خواندن و درک متون انگلیسی دانشجویان پزشکی را به طور چشمگیری تحت تاثیر قرار م...
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Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may...
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Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experim...
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Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10072344