KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

This section describes main characteristics of the autos data set and its attributes:

General information

Automobile Multi-class Imbalanced data set
Type	Imbalanced	Origin	Real world
Features	25	(Real / Integer / Nominal)	(15 / 0 / 10)
Instances	159	IR	16
% Positive instances	5.88	% Negative instances	94.12
Missing values?	No

Attribute description

Attribute	Domain	Attribute	Domain	Attribute	Domain
normalized-losses	[65.0, 256.0]	length	[141.1, 202.6]	bore	[2.54, 3.94]
make	{alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo}	width	[60.3, 71.7]	stroke	[2.07, 4.17]
fuel-type	{diesel, gas}	height	[49.4, 59.8]	compression-ratio	[7.0, 23.0]
aspiration	{std, turbo}	curb-weight	[1488.0, 4066.0]	horsepower	[48.0, 200.0]
num-of-doors	{four, two}	engine-type	{dohc, dohcv, l, ohc, ohcf, ohcv, rotor}	peak-rpm	[4150.0, 6600.0]
body-style	{hardtop, wagon, sedan, hatchback, convertible}	num-of-cylinders	{eight, five, four, six, three, twelve, two}	city-mpg	[15.0, 49.0]
drive-wheels	{4wd, fwd, rwd}	engine-size	[61.0, 258.0]	highway-mpg	[18.0, 54.0]
engine-location	{front, rear}	fuel-system	{1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi}	price	[5118.0, 35056.0]
wheel-base	[86.6, 115.6]	symboling	{-2, -1, 0, 1, 2, 3}

Additional information

An imbalanced version of the Automobile data set without missing values, where there are some classes with a small number of examples while other classes have a large number of examples.