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Found 4 papers, 1 papers with code
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy.
Return of the features. Efficient feature selection and interpretation for photometric redshifts
The methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.
Instrumentation and Methods for Astrophysics
Machine learning based data mining for Milky Way filamentary structures reconstruction
We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane.
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation
In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects.
