# Learn from every mistake! Hierarchical information combination in astronomy

## TL;DR

This paper introduces a hierarchical learning approach that combines multiple methods to improve classification and estimation tasks in astronomy, effectively correcting systematic errors and outperforming simple averaging techniques.

## Contribution

The paper presents a novel two-level hierarchical combination method for integrating multiple algorithms, enhancing performance in astronomical data analysis.

## Key findings

- Hierarchical combination improves accuracy over averaging methods.
- The approach corrects systematic errors in base methods.
- It is easy to train and applicable to Big Data in astronomy.

## Abstract

Throughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical "Big Data" era.