Decision trees with minimal costs

By Charles X. F. Ling, Qiang Yang, Jianning Wang, Shichao Zhang. International Conference on Machine Learning, 2004

323 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/3f067c56efc7c50b6cbd9629a60d244fdf03cbe2

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Citations in the corpus

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “The cost information is used to: (1) choose the best attribute to split the data [84,107]; and (2) determine whether a subtree should be pruned [18].”
Cost-sensitive boosting for classification of imbalanced data (2007, methodology): “The cost information is used to: (1) choose the best attribute to split the data [4,42]; and (2) determine whether a subtree should be pruned [43].”
Data mining with imbalanced class distributions: concepts and methods (2009, methodology): “Other approaches try to use the cost information for building the model, as in [21], which uses the cost information to construct a splitting criteria for decision tree building which minimizes the cost; or to construct algorithms which are no or little affected by costs, as the cost insensitive dec”
Cost-sensitive decision tree ensembles for effective imbalanced classification (2014, unknown): “Dataset Single CSTree [30] MCS from [27] SMOTEBagging [8] SMOTEBoost [8] IIvotes [5] EasyEnsemble [31] Proposed SE SP SE SP SE SP SE SP SE SP SE SP SE SP Pima 59.”
Finding Cost-Efficient Decision Trees (2014, background): “Information about dealing with missing values in data that have attribute costs and misclassification can be found in [23], [22], and [47].”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “In the family of decision tree classifiers, cost-based splitting criteria are used to minimize costs [129]; or cost information determine whether a subtree should be pruned [130].”
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, background): “Thus, the overfitting problem is avoided and causes the decision boundaries for the minority class to spread further into the majority class space.”
Active Learning Literature Survey (2009, background): “McCallum and Nigam (1998) also developed a density-weighted QBC approach for text classiﬁcation with na¨ıve Bayes, which is a special case of information density.”
Decision trees: a recent overview (2011, methodology): “Liu et al. (2009) study the problem of deriving ambiguous decision trees from data streams to cope with concept drift.”
Cost-Sensitive Decision Trees Applied to Medical Data (2007, methodology): “[15], [16], [17], [18], [19], [20].”