Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence Review - Special issue on lazy learning
Machine Learning - Special issue on learning with probabilistic representations
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using locally weighted learning to improve SMOreg for regression
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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In many real-world data mining applications, accurate class probability estimations are often required to make optimal decisions. For example, in direct marketing, we often need to deploy different promotion strategies to customers with different likelihood (probability) of buying some products. When our learning task is to build a model with accurate class probability estimations, C4.4 is the most popular one for achieving this task because of its efficiency and effect. In this paper, we present a locally weighted version of C4.4 to scale up its class probability estimation performance by combining locally weighted learning with C4.4. We call our improved algorithm locally weighted C4.4, simply LWC4.4. We experimentally tested LWC4.4 using the whole 36 UCI data sets selected by Weka, and compared it to other related algorithms: C4.4, NB, KNN, NBTree, and LWNB. The experimental results show that LWC4.4 significantly outperforms all the other algorithms in term of conditional log likelihood, simply CLL. Thus, our work provides an effective algorithm to produce accurate class probability estimation.