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Fraud detection is a difficult problem that can benefit from predictive modeling.
Classification and Regression Trees (CART) , volume 40
Breiman, Leo, Friedman, Jerome, Olshen, RA, and Stone, Charles J · 1984
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Using the adap learning algorithm to forecast the onset of diabetes mellitus
Smith, Jack W, Everhart, JE, Dickson, WC, Knowler, WC, and Johannes, RS · 1988
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Knowledge acquisition from examples via multiple models
Domingos, Pedro · 1997
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Random forests
Breiman, Leo · 2001
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Greedy function approximation: A gradient boosting machine
Friedman, Jerome H · 2001
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A data complexity analysis of comparative advantages of decision forest constructors
Ho, Tin Kam · 2002
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Extracting symbolic rules from trained neural network ensembles
Zhou, Zhi-Hua, Jiang, Y., and Chen, S.-F · 2003
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Model compression
Buciluǎ, Cristian, Caruana, Rich, and Niculescu-Mizil, Alexandru · 2006
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CTC: An alternative to extract explanation from bagging
Gurrutxaga, Ibai, Pérez, Jesús Ma, Arbelaitz, Olatz, Muguerza, Javier, Martín, José I, and Ansuategi, Ander · 2006
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Explanation and understanding
Keil, Frank C · 2006
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Combining multiple class distribution modified subsamples in a single tree
Pérez, Jesús M., Muguerza, Javier, Arbelaitz, Olatz, Gurrutxaga, Ibai, and Martín, José I · 2006
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Seeing the forest through the trees: Learning a comprehensible model from an ensemble
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Stiglic, Gregor and Kokol, Peter · 2007
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Maaten, Laurens van der and Hinton, Geoffrey · 2008
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Explaining classifications for individual instances
Robnik-Šikonja, Marko and Kononenko, Igor · 2008
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Štrumbelj, Erik, Kononenko, Igor, and Šikonja, M Robnik · 2009
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Altmann, André, Toloşi, Laura, Sander, Oliver, and Lengauer, Thomas · 2010
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Interpreting random forest classification models using a feature contribution method
Palczewska, Anna, Palczewski, Jan, Robinson, Richard Marchese, and Neagu, Daniel · 2014
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Goldstein, Alex, Kapelner, Adam, Bleich, Justin, and Pitkin, Emil · 2015
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Making tree ensembles interpretable
Hara, Satoshi and Hayashi, Kohei · 2016
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Interacting with predictions: Visual inspection of black-box machine learning models
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The mythos of model interpretability
Lipton, Zachary C · 2016
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Why should i trust you?: Explaining the predictions of any classifier
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