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Spring 2009 CSC 466: Knowledge Discovery from Data Alexander Dekhtyar Data Mining: Classification/Supervised Learning Definitions

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Abstract

Data. Consider a set A = {A1,...,An} of attributes, and an additional categorical attribute C, which we call a class attribute or category attribute. dom(C) = {c1,...,ck}. We call each value ci a class label or a category labwel. The learning dataset is a relational table D. Two formats: 1. Training (data)set. D has schema (A1,...,An,C), i.e., for each element of the dataset we are given its class label. 2. Test (data)set. D has schema (A1,...,An), i.e., the class labels of the records in D are not known. Classification Problem. Given a (training) dataset D, construct a classification/prediction function that correctly predicts the class label for every record in D. Classification function = prediction function = classification model = classifier. Supervised learning because training set contains class labels. Thus we can compare (supervise) predictions of our classifier. Classification Methodology Naïve Bayes. Estimation of probability that a record belongs to each class. 1 Neural Netowors. Graphical models that construct a ”separation function ” based on the training set data. Support Vector Machines (SVMs). Linear models for two-class classifiers

Topics: Association Rules
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.415.4539
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