Comparison of decision tree methods for finding active objects

TL;DR

This paper compares various decision tree algorithms for classifying active celestial objects using multi-wavelength data, highlighting their accuracy, speed, and interpretability for astronomical surveys.

Contribution

It evaluates multiple decision tree methods within WEKA for classifying active objects, providing insights into their performance and interpretability for astronomers.

Findings

ADTree has the highest accuracy.

Decision Stump is the fastest.

J48 balances accuracy and speed.

Abstract

The automated classification of objects from large catalogues or survey projects is an important task in many astronomical surveys. Faced with various classification algorithms, astronomers should select the method according to their requirements. Here we describe several kinds of decision trees for finding active objects by multi-wavelength data, such as REPTree, Random Tree, Decision Stump, Random Forest, J48, NBTree, AdTree. All decision tree approaches investigated are in the WEKA package. The classification performance of the methods is presented. In the process of classification by decision tree methods, the classification rules are easily obtained, moreover these rules are clear and easy to understand for astronomers. As a result, astronomers are inclined to prefer and apply them, thus know which attributes are important to discriminate celestial objects. The experimental results show that when various decision trees are applied in discriminating active objects (quasars, BL Lac objects and active galaxies) from non-active objects (stars and galaxies), ADTree is the best only in terms of accuracy, Decision Stump is the best only considering speed, J48 is the optimal choice considering both accuracy and speed.