Data set Editing by Ordered Projection

Article type: Research Article

Authors: Aguilar, Jesús S. | Riquelme, José C. | Toro, Miguel

Affiliations: Departamento de Lenguajes y Sistemas Informáticos, Facultad de Informática, Universidad de Sevilla, Avda. Reina Mercedes s/n. 41012 Sevilla, Spain. E-mail: [email protected], [email protected], [email protected]

Abstract: This paper presents a new approach to data set editing. The algorithm (EOP: Editing by Ordered Projection) has some interesting characteristics: important reduction of the number of examples from the database; lower computational cost (O(mnlogn)) with respect to other typical algorithms due to the absence of distance calculations; conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classifiers. The performance of EOP is analysed in two ways: percentage of reduction and classification. EOP has been compared to IB2, ENN and SHRINK concerning the percentage of reduction and the computational cost. In addition, we have analysed the accuracy of k-NN and C4.5 after applying the reduction techniques. An extensive empirical study using databases with continuous attributes from the UCI repository shows that EOP is a valuable preprocessing method for the later application of any axis-parallel learning algorithm.

Keywords: data mining, preprocessing techniques, data set editing, axis-parallel classifiers

DOI: 10.3233/IDA-2001-5504

Journal: Intelligent Data Analysis, vol. 5, no. 5, pp. 405-417, 2001