Weighted Substructure Mining for Image Analysis

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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “Consider the challenge of constructing a predictive model that discretizes the input space in the same way as decision trees [Breiman et al. (1984), Quinlan (1993)], decision lists [Rivest (1987)] or associative classifiers [Liu, Hsu and Ma (1998)].”
Discriminative Subsequence Mining for Action Classification (2007, methodology): “[13] proposed an image classification method termed Itemset Boosting that selects discriminative features from power sets of visual words.”
Mining Mid-level Features for Image Classification (2014, methodology): “Nevertheless, most attempts so far applying pattern mining to image classification [31,50,34,53] were not able to demonstrate”
Class segmentation and object localization with superpixel neighborhoods (2009, methodology): “Although we operate on a graph, we do not seek to mine discriminative substructures [26] or classify images based on the similarity of walks [14].”