WebDR: A Web Workbench for Data Reduction

Data reduction is a common preprocessing task in the context of the k nearest neighbour classification. This paper presents WebDR, a web-based application where several data reduction techniques have been integrated and can be executed on-line. WebDR allows the performance evaluation of the classification process through a web interface. Therefore, it can be used by the academia for educational and experimental purposes.

[1]  Georgios Evangelidis,et al.  Efficient editing and data abstraction by finding homogeneous clusters , 2015, Annals of Mathematics and Artificial Intelligence.

[2]  José Salvador Sánchez,et al.  High training set size reduction by space partitioning and prototype abstraction , 2004, Pattern Recognit..

[3]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[4]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[5]  Georgios Evangelidis,et al.  RHC: a non-parametric cluster-based data reduction for efficient k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\ , 2014, Pattern Analysis and Applications.

[6]  Georgios Evangelidis,et al.  AIB2: an abstraction data reduction technique based on IB2 , 2013, BCI '13.

[7]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[8]  E. K. Gannett,et al.  THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS , 1965 .

[9]  Belur V. Dasarathy,et al.  Nearest Neighbour Editing and Condensing Tools–Synergy Exploitation , 2000, Pattern Analysis & Applications.

[10]  Thomas Lukasiewicz Proceedings of the 7th International Symposium on the Foundations of Information and Knowledge Systems‚ FoIKS 2012‚ Kiel‚ Germany‚ March 5−9‚ 2012 , 2000 .

[11]  Sung-Bae Cho,et al.  Hybrid Artificial Intelligent Systems , 2015, Lecture Notes in Computer Science.

[12]  Georgios Evangelidis,et al.  EHC: Non-parametric Editing by Finding Homogeneous Clusters , 2014, FoIKS.

[13]  Georgios Evangelidis,et al.  Efficient dataset size reduction by finding homogeneous clusters , 2012, BCI '12.

[14]  José Francisco Martínez Trinidad,et al.  A new fast prototype selection method based on clustering , 2010, Pattern Analysis and Applications.

[15]  Francisco Herrera,et al.  Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[17]  Georgios Evangelidis,et al.  A Simple Noise-Tolerant Abstraction Algorithm for Fast k-NN Classification , 2012, HAIS.

[18]  Francisco Herrera,et al.  A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..