Cost-Sensitive Decision Trees Applied to Medical Data

By A. Freitas, A. Pereira, Pavel Brazdil. 2007

93 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “drug codes for analyzing chronic patients [116], medical diagnosis [117], diagnosis of sport injuries [118], prediction and sensitivity analysis of bubble dissolution time in 3D selective laser sintering [119], and part-of-speech tagging [120]; among others.”
Decision trees: a recent overview (2011, methodology): “The alternating decision tree (ADTree) is a generalization of decision trees, voted decision trees and voted decision stumps (Freund and Mason 1999, Pfahringer et al. 2001). A general alternating tree defines a classification rule as follows. An instance defines a set of paths in the alternating tre”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, methodology): “In third place, cost-sensitive approaches [10,17] combine the mentioned techniques in such a way that they incorporate different misclassi ﬁ cation costs for the instances in the learning algorithm.”