GeneticKNN: A Weighted KNN Approach Supported by Genetic Algorithm for Photometric Redshift Estimation of Quasars

TL;DR

This paper introduces GeneticKNN, a novel weighted KNN method supported by genetic algorithms, which improves photometric redshift estimation accuracy for quasars by optimizing features and combining median and mean redshift predictions.

Contribution

The paper proposes GeneticKNN, a new weighted KNN approach supported by genetic algorithms, enhancing redshift estimation accuracy over traditional methods.

Findings

GeneticKNN outperforms six traditional machine learning methods in redshift estimation.

Incorporating WISE magnitudes improves the accuracy of GeneticKNN.

GeneticKNN shows superior performance across all tested cases.

Abstract

We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by GA. This approach has two improvements compared to KNN: one is the feature weighted by GA; another is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors, i.e. $p\times z_{median} + (1-p)\times z_{mean}$. Based on the SDSS and SDSS-WISE quasar samples, we explore the performance of GeneticKNN for photometric redshift estimation, comparing with the other six traditional machine learning methods, i.e. Least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), Multi Layer Perceptrons (MLP), XGBoost, KNN and random forest. KNN and random forest show their superiority. Considering the easy implementation of KNN, we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars. Finally the performance of GeneticKNN is better than that of LASSO, SVR, MLP, XGBoost, KNN and random forest for all cases. Moreover the accuracy is better with the additional WISE magnitudes for the same method.