Lazy Classification Using an Optimized Instance-Based Learner

Abstract

Classification is a machine learning technique whose objective is the prediction of the class membership of data instances. There are numerous models currently available for performing classification, among which decision trees and artificial neural networks. In this article we describe the implementation of a new lazy classification model called similarity classifier. Given an out-of-sample instance, this model predicts its class by finding the training instances that are similar to it, and returning the most frequent class among these instances. The classifier was implemented using Weka’s data mining API, and is available for download. Its performance, according to accuracy and speed metrics, compares relatively well with that of well-established classifiers such as nearest neighbor models or support vector machines. For this reason, the similarity classifier can become a useful instrument in a data mining practitioner’s tool set.