Titlebook: Discovery Science; 18th International C Nathalie Japkowicz,Stan Matwin Conference proceedings 2015 Springer International Publishing Switze

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Resolution Transfer in Cancer Classification Based on Amplification Patterns,tional machine learning and data mining algorithms can handle data only in a single representation in their standard form. In this contribution, we address an important challenge encountered in data analysis: what to do when the data to be analyzed are represented differently with regards to the res

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exophthalmos

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Ptosis

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Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis, relationships within the data. When these datasets also includes temporal and geospatial components, the challenges in analyzing the data become even more difficult. A number of visualization approaches have been developed and studied to support the exploration and analysis among such datasets, inc

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lanugo

Ensembles of Extremely Randomized Trees for Multi-target Regression,on (MTR). In contrast to standard regression, where the output is a single scalar value, in MTR the output is a data structure – a tuple/vector of continuous variables. The task of MTR is recently gaining increasing interest by the research community due to its applicability in a practically relevan

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Clustering-Based Optimised Probabilistic Active Learning (COPAL),ling of the most valuable instances gain in importance. A particular challenge is the active learning of arbitrary, user-specified adaptive classifiers in evolving datastreams.We address this challenge by proposing a novel clustering-based optimised probabilistic active learning (COPAL) approach for