PERFORMANCE INVESTIGATION OF GENERATIVE MODELS FOR CLASSIFICATION OF ALCOHOLSJournal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.5, No. 6)
Publication Date: 2016-06-30
Authors : Kumar Shashvat; Raman Chadha; Arshpreet Kaur;
Page : 110-118
Keywords : Classification; Generative Models; Naive Bayes; Linear Discriminant Analysis;
Classificati on is the process related to categorization, the process in which ideas and objects are understood. It helps in clear identification of species for classification of various chemical compounds like Alc o hol, Wine various discriminative approaches have been used .Discriminative methods offer good predictive performance and have been widely used in many applications but are unable to make efficient use of the unlabelled information. In such scenarios genera tive approaches have better applicability, as they are able to knob problems, such as in scenarios where variability in the range of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an uncertain probability density function. In this paper the generative models like Linear Discriminant Analysis and Naive Bayes have been used for classification of the alcohols . Linear Discriminant Analysis is a method u sed in data classification, pattern recognition and machine learning to discover a linear combination of features that characterizes or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification algorithm base on Ba yes rule and a set of conditional independence supposition . Naive Bayes classifiers are highly scalable, requiring a number of constraints linear in the number of variables (features/predictors) in a learning predicament. The main advantages of using the g enerative models are usually a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables . The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The experimental results have proven that the overall performance of the Linear Discriminanat Analysis was better in comparison to the Naive Bayes Classifier on alc o hol dataset.
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