vehicle: Statlog (Vehicle Silhouettes) Dataset.

Description

The purpose is to classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.

Usage

data(vehicle)

Arguments

Format

A data frame with 846 rows and 19 variables:

COMPACTNESS: (average perim)**2/area.
CIRCULARITY: (average radius)**2/area.
DISTANCE CIRCULARITY: area/(av.distance from border)**2.
RADIUS RATIO: (max.rad-min.rad)/av.radius.
PR.AXIS ASPECT RATIO: (minor axis)/(major axis).
MAX.LENGTH ASPECT RATIO: (length perp. max length)/(max length).
SCATTER RATIO: (inertia about minor axis)/(inertia about major axis).
ELONGATEDNESS: area/(shrink width)**2.
PR.AXIS RECTANGULARITY: area/(pr.axis length*pr.axis width).
MAX.LENGTH RECTANGULARITY: area/(max.length*length perp. to this).
SCALED VARIANCE ALONG MAJOR AXIS: (2nd order moment about minor axis)/area.
SCALED VARIANCE ALONG MINOR AXIS: (2nd order moment about major axis)/area.
SCALED RADIUS OF GYRATION: (mavar+mivar)/area.
SKEWNESS ABOUT MAJOR AXIS: (3rd order moment about major axis)/sigma_min**3.
SKEWNESS ABOUT MINOR AXIS: (3rd order moment about minor axis)/sigma_maj**3.
KURTOSIS ABOUT MINOR AXIS: (4th order moment about major axis)/sigma_min**4.
KURTOSIS ABOUT MAJOR AXIS: (4th order moment about minor axis)/sigma_maj**4.
HOLLOWS RATIO: (area of hollows)/(area of bounding polygon).
class: 4 classes, OPEL, SAAB, BUS, VAN.