<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.</p></abstract>

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.

A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do...

A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

Data publikacji: 15 cze 2020

Zakres stron: 287 - 298

Otrzymano: 05 lis 2019

Przyjęty: 18 maj 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0019

Słowa kluczowe
data stream mining, random forest, features importance

© 2020 Piotr Duda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

Data publikacji: 15 cze 2020

Zakres stron: 287 - 298

Otrzymano: 05 lis 2019

Przyjęty: 18 maj 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0019

Słowa kluczowedata stream mining, random forest, features importance

© 2020 Piotr Duda et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
data stream mining, random forest, features importance