Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods

Moisture transport by Atlantic tropical cyclones onto the North American continent

Tropical Cyclones (TCs) are an important source of freshwater for the North American continent. Many studies have tried to estimate this contribution by identifying TC-induced precipitation events, but few have explicitly diagnosed the moisture fluxes across continental boundaries. We design a set of attribution schemes to isolate the column-integrated moisture fluxes that are directly associated with TCs and to quantify the flux onto the North American Continent due to TCs. Averaged over the 2004–2012 hurricane seasons and integrated over the western, southern and eastern coasts of North America, the seven schemes attribute 7 to 18 % (mean 14 %) of total net onshore flux to Atlantic TCs. A reduced contribution of 10 % (range 9 to 11 %) was found for the 1980–2003 period, though only two schemes could be applied to this earlier period. Over the whole 1980–2012 period, a further 8 % (range 6 to 9 % from two schemes) was attributed to East Pacific TCs, resulting in a total TC contribution of 19 % (range 17 to 22 %) to the ocean-to-land moisture transport onto the North American continent between May and November. Analysis of the attribution uncertainties suggests that incorporating details of individual TC size and shape adds limited value to a fixed radius approach and TC positional errors in the ERA-Interim reanalysis do not affect the results significantly, but biases in peak wind speeds and TC sizes may lead to underestimates of moisture transport. The interannual variability does not appear to be strongly related to the El Nino-Southern Oscillation phenomenon