In this chapter, you will learn the popular classification algorithms written in the R language. Empirical classifier performance and accuracy benchmarks are also included. Along with the introduction of various classification algorithms, b will also learn various ways to improve the classifier and so on.
Classification has massive applications in modern life. With the exponential growth of the information dataset, there is a need for high performance classification algorithms to judge an event/object belonging to a predefined categories set. Such algorithms have unlimited opportunity for implementation in a wide variety of industries such as bioinformatics, cybercrime, and banking. Successful classification algorithms use predefined categories from training information datasets to predict the unknown category for a single event given a common set of features.
Along with the continual growth of computer science, the classification algorithms need to be implemented on many diverse platforms including distributed infrastructure, cloud environment, real-time devices, and parallel computing systems.
In this chapter, we will cover the following topics:
- Classification
- Generic decision tree introduction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Spam e-mail identification and Naïve Bayes classification
- Rule-based classification and the player types in computer games
Given a set of predefined class labels, the task of classification is to assign each data object of the input dataset with a label using the classifier's training model. Typically, the input can be a discrete or continuous value, but the output is discrete binary or nominal value and so forth. Classification algorithms are often described as learning models or functions, in which x is a tuple of attribute set with discrete or continuous value, and y is an attribute with discrete value such as categorical labels.
This function can also be treated as a classification model. It can be used to distinguish objects belonging to different classes or to predict the class of a new tuple or y in the above (x, y). In another point of view, classification algorithms are targeted to find a model from the input data, and apply this model to future classification usage predictions when given a common set of attributes.
Generally speaking, 
is a set of attributes selected as the input for the classification system. There are special algorithms used to select only the useful attributes from this set to ensure the efficiency of the classification system.
Almost any classification tasks need this preprocessing procedure, but the exact means vary from case to case. Here are three mainstream methods applied:
- Data cleaning
- Relevance analysis
- Data transformation and reduction
A standard classification process often includes two steps. The classification model with the higher accepted accuracy is accepted as classifier to classify a dataset in production. The following two steps are illustrated with an example in the diagram:
In the following sections, we will introduce some classification algorithms with different designs.