# Online Local Boosting: improving performance in online decision trees

## TL;DR

This paper introduces Online Local Boosting (OLBoost), a novel method that enhances online decision trees' predictive accuracy by boosting small regions of data without increasing memory or processing costs.

## Contribution

OLBoost is a new boosting technique that improves online decision tree performance by focusing on local regions, without altering tree structures.

## Key findings

- OLBoost significantly improves predictive accuracy of online decision trees.
- Smaller trees with OLBoost can outperform larger trees.
- OLBoost maintains low memory and processing costs.

## Abstract

As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current algorithms for data stream mining have high processing and memory costs. Often, the higher the predictive performance, the higher these costs. To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees. For such, OLBoost applies a boosting to small separate regions of the instances space. Experimental results presented in this paper show that by using OLBoost the online learning decision tree algorithms can significantly improve their predictive performance. Additionally, it can make smaller trees perform as good or better than larger trees.