Logistic Model Trees

By Niels Landwehr, Mark A. Hall, Eibe Frank. 2003

1,526 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, methodology): “The LMT is a classification tree method which combines logistic regression and decision tree learning algorithms (Landwehr et al. 2005).”
Generative-Discriminative Basis Learning for Medical Imaging (2012, methodology): “These features were fed into a supervised-classiﬁer (Logistic Model Trees [54]) and a semi-supervised classiﬁer (linear Laplacian SVM [67]) to produces labels.”
Do we need hundreds of classifiers to solve real world classification problems? (2014, methodology): “SimpleLogistic w learns linear logistic regression models (Landwehr et al., 2005) for classification.”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, methodology): “During the fitting of the LR functions, the logitboost algorithm (Landwehr et al. 2005) is used at a tree node.”
Towards Measuring Membership Privacy (2017, methodology): “We build a logistic regression model using Weka’s implementation of Logistic model trees [20].”
Personal recognition using hand shape and texture (2006, methodology): “This is not surprising as LMT algorithm has shown [27] to be often more accurate than and always resulting in a tree of small size than those from .”
Logistic Model Tree Extraction From Artificial Neural Networks (2007, methodology): “…10.1109/TSMCB.2007.895334 can lead to new insights into patterns and dependencies within the data; from symbolic knowledge, it is easier to see which features of the data are the most important; and the explanation of a decision is essential for many applications, such as safetycritical systems.”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, methodology): “These success rates put the SimpleLogistic classifier roughly in the middle of the other classifiers in terms of vulnerability to attacks.”
A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, methodology): “The splitting process of the tree is performed using the information gain technique; then, in the tree nodes, the LogitBoost algorithm (Landwehr et al. 2005) is used for LR fitting (Tien Bui et al.”
dtControl: decision tree learning algorithms for controller representation (2020, neutral): “els [17], piece-wise functions [18] or Support-Vector Machines [19, 20].”