Profiling Astronomical Objects Using Unsupervised Learning Approach

作     者：Theerapat Sangpetch Tossapon Boongoen Natthakan Iam-On 

作者机构：Center of Excellence in AI and Emerging TechnologiesSchool of Information TechnologyMae Fah Luang UniversityChiang Rai57100Thailand Department of Computer ScienceAberystwyth UniversityAberystwythCeredigionUK 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第1期

页      面：1641-1655页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Security BigData Fusion Project(Office of theMinistry of Higher Education Science Research and Innovation).The corresponding author is the project PI 

主　　题：Astronomy sky survey light curve data classification data clustering 

摘      要：Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science *** of a manual inspection,various automated systems are invented to satisfy the need,including the classification of light curve profiles.A specific Kaggle competition,namely Photometric LSST Astronomical Time-Series Classification Challenge(PLAsTiCC),is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope(LSST)*** all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined *** this challenge focuses on developing a predictive model that is robust to classifying unseen data,those previous attempts similarly encounter the lack of discriminate features,since distribution of training and actual test datasets are largely *** a result,well-known classification algorithms prove to be sub-optimal,while more complicated feature extraction techniques may help to slightly boost the predictive *** such a burden,this research is set to explore an unsupervised alternative to the difficult quest,where common classifiers fail to reach the 50%accuracy mark.A clustering technique is exploited to transform the space of training data,from which a more accurate classifier can be *** addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning,a multiple-clustering alternative is also introduced with improved *** fact,it is able to yield a higher accuracy rate of 58.32%from 51.36%that is obtained using a simple *** this difficult problem,it is rather good considering for those achieved by well-known models like support vector machine(SVM)with 51.80%and Naive Bayes(NB)with only 2.92%.