期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：10
出版社：S.S. Mishra
摘要：Classic decision sapling classifiers assist data whose values are usually known along with precise. We extend such classifiers to manage da ta using uncertain information .Value scepticism arises in numerous applications throughout the data collection process. Example reasons for uncertainty consist of measurement/quantization problems, data staleness, along with multiple repeated measurements. Along with uncertainty, the worth of any data item is normally represented certainly not by a unitary value, yet by numerous values being created a likelihood distribution. As opposed to abstracting unsure data by means of statistical derivatives (such while mean along with median), we see that the accuracy of a decision sapling classifier is usually much improved if the "complete information" of a data item (taking into account the likelihood density operate (pdf)) is utilized. We extend classical conclusion tree constructing algorithms to manage data tuples using uncertain prices. Extensive experiments have been conducted which usually show that this resulting classifiers will be more accurate than those utilizing value averages.
