Paper Info
Title
Machine learning based data mining for Milky Way filamentary structures reconstruction
Abstract
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. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary a posteriori analysis of derived filament physical parameters, themethod appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.
Year
DOI
Venue
2015
10.1007/978-3-319-33747-0_3
ADVANCES IN NEURAL NETWORKS: COMPUTATIONAL INTELLIGENCE FOR ICT
Keywords
Field
DocType
Galaxy evolution,Machine learning,Random forest
Data mining,Protein filament,A priori and a posteriori,Feature extraction,Artificial intelligence,Pixel,Galactic plane,Random forest,Shape reconstruction,Milky Way,Machine learning,Physics
Journal
Volume
ISSN
Citations 
54
2190-3018
1
PageRank 
References 
Authors
0.36
2
10
Name
Order
Citations
PageRank
giuseppe riccio143.21
S. Cavuoti263.75
e schisano311.04
Massimo Brescia4148.41
a mercurio510.36
d elia611.04
m benedettini710.70
s pezzuto810.36
s molinari911.04
anna maria di giorgio1011.38