Decision Trees for Mining Data Streams Based on the McDiarmid's Bound

Authors:
Leszek Rutkowski;Lena Pietruczuk;Piotr Duda;Maciej Jaworski
Affiliations:
Czestochowa University of Technology, Czestochowa;Czestochowa University of Technology, Czestochowa;Czestochowa University of Technology, Czestochowa;Czestochowa University of Technology, Czestochowa
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2013

Citing 0
Cited 3

Soft-CsGDT: soft cost-sensitive Gaussian decision tree for cost-sensitive classification of data streams

Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Mining Top-K Rank Frequent Patterns in Data Streams A Tree Based Approach with Ternary Function and Ternary Feature Vector

Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
The CART decision tree for mining data streams

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. In the literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g., information gain or Gini index. In this paper, it is shown that the Hoeffding's inequality is not appropriate to solve the underlying problem. We prove two theorems presenting the McDiarmid's bound for both the information gain, used in ID3 algorithm, and for Gini index, used in Classification and Regression Trees (CART) algorithm. The results of the paper guarantee that a decision tree learning system, applied to data streams and based on the McDiarmid's bound, has the property that its output is nearly identical to that of a conventional learner. The results of the paper have a great impact on the state of the art of mining data streams and various developed so far methods and algorithms should be reconsidered.