Download:
|
by Byung-hoon Park, Hillol Kargupta
In Proceedings of the 7th Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM SIGMOD
http://www.bell-labs.com/user/minos/DMKD02/Papers/park.ps
Add To MetaCart
Abstract:
Ensemble learning is frequently used for classication and other related applications in data mining. It generates multiple models and produces the nal classi cation by aggregating the outputs of the dierent models in the ensemble. However, large ensembles are often hard to interpret and dicult to translate into action-able knowledge. This paper considers the construction of a decision tree from the Fourier spectrum of an ensemble model within a user-dened range of errors. The Fourier spectrum of an ensemble of decision trees retains all the necessary information that can be used to construct a simpler \informative " decision tree. This approach can be eectively used for building ensemble-based classiers from both static data sets and data streams. 1
Citations
|
1453
|
Bagging Predictors
– Breiman
- 1996
|
|
296
|
An experimental ! comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, submitted to Machine Learning
– Dietterich
- 1998
|
|
199
|
Constant depth circuits, Fourier transform, and learnability
– Linial, Mansour, et al.
- 1993
|
|
136
|
Genetic algorithms and Walsh functions: Part I, a gentle introduction
– Goldberg
- 1989
|
|
40
|
An empirical comparison of voting classi algorithms: Bagging, boosting, and variants
– Bauer, Kohavi
- 1999
|
|
21
|
Pasting small votes for classification in large databases and on-line
– Breiman
- 1999
|
|
20
|
A principal component approach to combining regression estimates
– Merz
- 1999
|
|
8
|
A fourier analysis-based approach to learn classifier from distributed heterogeneous data
– Park, Ayyagari, et al.
- 2001
|
|
8
|
Distributed learning with knowledge probing: A new framework for distributed data mining
– Guo, Sutiwaraphun
- 2000
|
|
6
|
Knowledge Discovery from Heterogeneous Data Streams Using Fourier Spectrum of Decision Trees
– Park
- 2001
|
|
3
|
Visual classi An interactive approach to decision tree construction
– Ankerst, Elsen, et al.
- 1999
|
|
2
|
Mobimine:monitoring the stock market from a PDA
– Kargupta, Park, et al.
- 2002
|
|
2
|
Pasting small votes for classi cation in large databases and on-line
– Breiman
- 1999
|
|
2
|
Pruning classi in a distributed meta-learning system
– Prodromidis, Stolfo, et al.
- 1998
|
