Supervised Classification: Quite a Brief Overview

TL;DR

This paper provides a concise overview of supervised classification, covering fundamental concepts, techniques, object representation, evaluation, and variations, aimed at introducing practitioners to the field.

Contribution

It offers a brief, accessible introduction to supervised classification, summarizing key methods and issues for newcomers and practitioners.

Findings

Summarizes core supervised classification techniques

Discusses object representation and classifier evaluation

Highlights variations and practical considerations

Abstract

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.