Performance evaluation of incremental decision tree learning under noisy data streams. (6th June 2013)

• Record Type: Journal Article
• Authors: Yang, Hang; Fong, Simon
• Is Part Of: International journal of computer applications technology. Volume 47:Number 2/3(2013)
• Page Start: 206
• Page End: 214
• Publication Date: 2013-06-06
• Subjects: big data -- data streams -- classification models -- decision trees -- noisy data -- performance evaluation -- incremental learning -- multi–objective optimisation; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

Big data has become a significant problem in software applications nowadays. Extracting classification model from such data requires an incremental learning process. The model should update when new data arrive, without re–scanning historical data. A single–pass algorithm suits continuously arrival data environment. However, one practical and important aspect that has gone relatively unstudied is noisy data streams. Such data are inevitable in real–world applications. This paper presents a new classification model with a single decision tree, so called incrementally Optimised Very Fast Decision Tree (iOVFDT) that embeds multi–objectives incremental optimisation and functional tree leaf. In the performance evaluation, noisy values were added into synthetic data. This evaluation investigated the performance under noisy data scenario. The result showed that iOVFDT outperforms the existing algorithms.