Vilalta, Ricardo2017-04-172017-04-17May 20152015-05May 2015http://hdl.handle.net/10657/1705With the increasing amounts of astronomical data being gathered, it is becoming more crucial for machine learning techniques to be employed for star classification. Classical Cepheid variable stars can be grouped into several classes, such as fundamental-mode, first-overtone, and second-overtone. Each class has distinctive features, and the light curves of the stars can be analyzed for these features in order to be used in automatic classification. Here, we focus on developing a number of features to be used with the following machine learning methods: Multilayer Perceptron, Naïve Bayes, J48 Decision Trees, and Random Forest. We use the OGLE (Optical Gravitational Lensing Experiment) datasets of Classical Cepheid variable stars in the Large Magellanic Cloud and the Small Magellanic Cloud. Our findings indicate that the Multilayer Perceptron is an excellent method for approaching this problem, as it outperformed the other machine learning methods. We also identify a number of useful features using Information Gain and Gain Ratio. Specifically, the newly developed features to measure symmetry had high classification power.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Machine learningClassificationLarge Magellanic CloudSmall Magellanic CloudCepheidsCepheidsVariable starsVariable starsAstronomyComputer scienceOGLEDataFundamental modeFirst overtoneSecond overtoneFundamental modeFirst overtoneSecond overtoneMultilayer PerceptronNeural networksNeurosciencesInformation gainGain RatioClassifiersStarStars2017-04-17Thesisborn digital