A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

By Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih. 2000

1,264 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Interpretable classification models for recidivism prediction (2015, methodology): “0R is also observed by, for instance, Lim et al. 2000).”
Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “The blend of different evaluation tools is expected to endorse the development of analytical research [46].”
Activity-Aware Mental Stress Detection Using Physiological Sensors (2010, background): “Decision Tree is recognized as one of the classiﬁcation methods with low computational complexity [14].”
Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, background): “, [71] (STALOG project with 23 different classifiers evaluated in 22 real datasets); Lim et al [72] (33 classifiers evaluated on 16 real data sets) and Meyer et al.”
Tree Induction for Probability-Based Ranking (2003, positive): “Tree-induction programs are fast and effective ( Lim, Loh, & Shih, 2000 ).”
YaDT: yet another decision tree builder (2004, background): “The survey [ 5 ] shows that it provides good classification accuracy and is the fastest among the compared main-memory classification algorithms.”
Discrete support vector decision trees via tabu search (2004, methodology): “…our classi0ers is broad, these methods were selected because they represent a wide range of eDective approaches; furthermore, from the results of a recent benchmark of thirty-three classi0cation methods (Lim et al., 2000) it appears that Quest emerged as the leading classi0er on most datasets.”
Classification and regression trees (2011, positive): “10 , 11 , 28 The computational speed of C4.5 is almost always the fastest, whereas RPART can be fast or extremely slow, with the latter occurring when Y takes more than two values and there are unordered variables taking many values (owing to its coding, the RPART software may give incorrect…”
Theoretical Comparison between the Gini Index and Information Gain Criteria (2004, background): “In [18], the authors proposed a measure for the distance between the bias of two evaluation metrics and gave numerical approximations of it.”