Temel Kavramlar
Distance-based classifier DistClassiPy enhances interpretability and reduces computational costs in light curve classification.
Özet
The rise of synoptic sky surveys has led to a data-intensive challenge in time-domain astronomy. Machine learning is essential for automating object classification. The new distance-based classifier, DistClassiPy, uses 18 distance metrics to classify variable stars' light curves effectively. Feature extraction and dimensionality reduction improve model performance and interpretability.
Introduction
Time-domain astronomy growth due to large-scale sky surveys.
Need for machine learning in object classification.
Data Extraction
Dataset from Zwicky Transient Facility DR15.
Features extracted using lc classifier module.
Feature Selection and Dimensionality Reduction
Reduced feature space from 112 to 31 features.
Sequential Feature Selection further reduced features based on effectiveness.
Classification Algorithm
Custom algorithm DistClassiPy inspired by k-Nearest Neighbours.
Training involves computing median and standard deviation per class.
Confidence Measures
Three confidence parameters: inverse of total distance, inverse of scaled distances, KDE probability.
Random Forest Classifier
Benchmark comparison with RFC using 100 estimators and maximum depth of 3.
Results
Performance evaluated using F1 score in multi-class classification tasks with Clark and Canberra metrics showing high accuracy.
İstatistikler
Using 18 distance metrics applied to a catalog of 6,000 variable stars in 10 classes.
The final set of features selected through Sequential Feature Selection (SFS) process for each metric and classification task.