Data Complexity in Pattern Recognition

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A unifying view on dataset shift in classification (2012, background): “The machine learning community has analyzed data quality in classification problems from different perspectives, including data complexity [29] [7], missing values [19] [21] [39], noise [11] [64] [58] [38], imbalance [52] [27] [53] and, as is the case with this paper, dataset shift [4] [44] [14].”
How Complex Is Your Classification Problem? (2018, background): “of regions of low density that will be arbitrarily classiﬁed. 2.5.1 Average number of features per dimension (T2) Originally, T2 divides the number of examples in the dataset by their dimensionality [Basu and Ho, 2006]. In this paper we take the inverse of this formulation in order to obtain higher ”
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, methodology): “These measures were proposed by Ho and Basu [12] and have been used in recent studies for extracting the domains of competence of an algorithm [53,14].”