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Environmental studies form an increasingly popular application domain for machine learning and data mining techniques. In this paper we consider two applications of decision tree learning in the domain of river water quality: a) the simultaneous prediction of multiple physico-chemical properties of the water from its biological properties using a single decision tree (as opposed to learning a different tree for each different property) and b) the prediction of past physico-chemical properties of the river water from its current biological properties.
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Thus, we introduce the extended averaging method to solve these two problems in the subsequent subsections. 2 Extended Moving Average for Continuous Attributes In this extension, we first focus on how moving average methods remove noise. The key idea is that a window parameter w is closely related with periodicity. If w is larger, then the periodical behavior whose time-constant is lower than w will be removed. Usually, a spike by noise is observed as a single event and this effect will be removed when w is taken as a large value.