3 research outputs found
Feature importance for machine learning redshifts applied to SDSS galaxies
We present an analysis of importance feature selection applied to photometric
redshift estimation using the machine learning architecture Decision Trees with
the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85
easily measured (or derived) photometric quantities (or `features') and
spectroscopic redshifts for almost two million galaxies from the Sloan Digital
Sky Survey Data Release 10. After identifying which features have the most
predictive power, we use standard artificial Neural Networks (aNN) to show that
the addition of these features, in combination with the standard magnitudes and
colours, improves the machine learning redshift estimate by 18% and decreases
the catastrophic outlier rate by 32%. We further compare the redshift estimate
using RDF with those from two different aNNs, and with photometric redshifts
available from the SDSS. We find that the RDF requires orders of magnitude less
computation time than the aNNs to obtain a machine learning redshift while
reducing both the catastrophic outlier rate by up to 43%, and the redshift
error by up to 25%. When compared to the SDSS photometric redshifts, the RDF
machine learning redshifts both decreases the standard deviation of residuals
scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of
catastrophic outliers by 57% from 2.32% to 0.99%.Comment: 10 pages, 4 figures, updated to match version accepted in MNRA
Stacking for machine learning redshifts applied to SDSS galaxies
We present an analysis of a general machine learning technique called
'stacking' for the estimation of photometric redshifts. Stacking techniques can
feed the photometric redshift estimate, as output by a base algorithm, back
into the same algorithm as an additional input feature in a subsequent learning
round. We shown how all tested base algorithms benefit from at least one
additional stacking round (or layer). To demonstrate the benefit of stacking,
we apply the method to both unsupervised machine learning techniques based on
self-organising maps (SOMs), and supervised machine learning methods based on
decision trees. We explore a range of stacking architectures, such as the
number of layers and the number of base learners per layer. Finally we explore
the effectiveness of stacking even when using a successful algorithm such as
AdaBoost. We observe a significant improvement of between 1.9% and 21% on all
computed metrics when stacking is applied to weak learners (such as SOMs and
decision trees). When applied to strong learning algorithms (such as AdaBoost)
the ratio of improvement shrinks, but still remains positive and is between
0.4% and 2.5% for the explored metrics and comes at almost no additional
computational cost.Comment: 13 pages, 3 tables, 7 figures version accepted by MNRAS, minor text
updates. Results and conclusions unchange