Classification and Error Estimation

doi:10.1117/3.2540669.ch1

Book: Optimal Bayesian Classification

Author(s): Lori A. Dalton, Edward R. Dougherty

Published: 2020

https://doi.org/10.1117/3.2540669.ch1

Author Affiliations +

Abstract

A classifier operates on a vector of features, which are random variables, and outputs a decision as to which class the feature vector belongs. We consider binary classification, meaning that the decision is between two classes. Typically, a classifier is designed and its error estimated from sample data. Two basic questions arise. First, given a set of features, how does one design a classifier from the sample data that provides good classification over the general population? Second, how does one estimate the error of a designed classifier from the data? This book examines both classifier design and error estimation when one is given prior knowledge regarding the population. The first chapter provides basic background knowledge.