论文信息 - Communication efficient construction of decision trees over heterogeneously distributed data

Communication efficient construction of decision trees over heterogeneously distributed data

We present an algorithm designed to efficiently construct a decision tree over heterogeneously distributed data without centralizing. We compare our algorithm against a standard centralized decision tree implementation in terms of accuracy as well as the communication complexity. Our experimental results show that by using only 20% of the communication cost necessary to centralize the data we can achieve trees with accuracy at least 80% of the trees produced by the centralized version.

[1] D. Opitz,et al. Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[2] Nitesh V. Chawla,et al. Learning Rules from Distributed Data , 1999, Large-Scale Parallel Data Mining.

[3] Wenliang Du,et al. Building decision tree classifier on private data , 2002 .

[4] Vasant Honavar,et al. Decision Tree Induction from Distributed Heterogeneous Autonomous Data Sources , 2003 .

[5] Hillol Kargupta,et al. Distributed Data Mining: Algorithms, Systems, and Applications , 2003 .

[6] Yelena Yesha,et al. Data Mining: Next Generation Challenges and Future Directions , 2004 .

[7] Eleonora Riva Sanseverino,et al. Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique , 2001, Applied Intelligence.

[8] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[9] Santosh S. Vempala,et al. An algorithmic theory of learning: Robust concepts and random projection , 1999, Machine Learning.