data(RoughSetData)
. The following is a
description of each datasets.It is simple data taken from (Komorowski et al, 1999) where all the attributes have nominal values. It consists of eight objects with four conditional attributes and one decision attribute. The detailed description of each attribute is as follows:
The housing dataset
This data was taken from the Boston housing dataset located at the UCI Machine Learning repository, available at http://www.ics.uci.edu. It was first created by (Harrison, D. and Rubinfeld, D.L., 1978). It contains 506 objects with 13 conditional attributes and one decision attribute. Furthermore, it should be noted that the housing dataset is a regression dataset which means that the decision attribute has continuous values. The conditional attributes contain both continuous and nominal attributes. The following is a description of each attribute:
The wine dataset
This dataset is a classification dataset introduced first by (Forina, M. et al) which is commonly used as benchmark for simulation in the machine learning area. Additionally, it is available at the KEEL dataset repository (J. Alcala-Fdez, 2009), available at http://www.keel.es/. It consists of 178 instances with 13 conditional attributes and one decision attribute where all conditional attributes have continuous values. The description of each attribute is as follows:
The pima dataset
It was taken from the pima Indians diabetes dataset which is available at the KEEL dataset repository (J. Alcala-Fdez, 2009), available at http://www.keel.es/. It was first created by National Institute of Diabetes and Digestive and Kidney Diseases. It contains 768 objects with 8 continuous conditional attributes. The description of each attribute is as follows:
D. Harrison, and D. L. Rubinfeld, "Hedonic Prices and the Demand for Clean Air", J. Environ. Economics & Management, vol.5, 81-102 (1978).
J. Alcala-Fdez, L. Sanchez, S. Garcia, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernandez, and F. Herrera, "KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems", Soft Computing vol. 13, no. 3, p. 307 - 318 (2009).
J. Komorowski, Z. Pawlak, L. Polwski, and A. Skowron, "Rough Sets: A Tutorial", In S. K. Pal and A. Skowron, editors, Rough Fuzzy Hybridization, A New Trend in Decision Making, pp. 3 - 98, Singopore, Springer (1999).