Soybean: Soybean dataset

In partitionMap: Partition Maps

Description

There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The values for attributes are encoded numerically, with the first value encoded as “0,” the second as “1,” and so forth. Observations with missing values in the original dataset have been removed.

Usage

data(Soybean)

Format

A data frame with 562 observations on the following 36 variables.

Y

the 19 classes

date

apr(0),may(1),june(2),july(3),aug(4),sept(5),oct(6)

plant.stand

normal(0),lt-normal(1)

precip

lt-norm(0),norm(1),gt-norm(2)

temp

lt-norm(0),norm(1),gt-norm(2)

hail

yes(0),no(1)

crop.hist

dif-lst-yr(0),s-l-y(1),s-l-2-y(2), s-l-7-y(3)

area.dam

scatter(0),low-area(1),upper-ar(2),whole-field(3)

sever

minor(0),pot-severe(1),severe(2)

seed.tmt

none(0),fungicide(1),other(2)

germ

90-100(0),80-89(1),lt-80(2)

plant.growth

norm(0),abnorm(1)

leaves

norm(0),abnorm(1)

leaf.halo

absent(0),yellow-halos(1),no-yellow-halos(2)

leaf.marg

w-s-marg(0),no-w-s-marg(1),dna(2)

leaf.size

lt-1/8(0),gt-1/8(1),dna(2)

leaf.shread

absent(0),present(1)

leaf.malf

absent(0),present(1)

leaf.mild

absent(0),upper-surf(1),lower-surf(2)

stem

norm(0),abnorm(1)

lodging

yes(0),no(1)

stem.cankers

absent(0),below-soil(1),above-s(2),ab-sec-nde(3)

canker.lesion

dna(0),brown(1),dk-brown-blk(2),tan(3)

fruiting.bodies

absent(0),present(1)

ext.decay

absent(0),firm-and-dry(1),watery(2)

mycelium

absent(0),present(1)

int.discolor

none(0),brown(1),black(2)

sclerotia

absent(0),present(1)

fruit.pods

norm(0),diseased(1),few-present(2),dna(3)

fruit.spots

absent(0),col(1),br-w/blk-speck(2),distort(3),dna(4)

seed

norm(0),abnorm(1)

mold.growth

absent(0),present(1)

seed.discolor

absent(0),present(1)

seed.size

norm(0),lt-norm(1)

shriveling

absent(0),present(1)

roots

norm(0),rotted(1),galls-cysts(2)

Source

Source: R.S. Michalski and R.L. Chilausky "Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis", International Journal of Policy Analysis and Information Systems, Vol. 4, No. 2, 1980.

Donor: Ming Tan & Jeff Schlimmer ([e-mail])

These data have been taken from the UCI Repository Of Machine Learning Databases at

* <URL: ftp://ftp.ics.uci.edu/pub/machine-learning-databases>

* <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

and were converted to R format by Evgenia Dimitriadou, as were copied from the mlbench package.

References

Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning (pp. 121-134). Ann Arbor, Michigan: Morgan Kaufmann. - IWN recorded a 97.1percent classification accuracy - 290 training and 340 test instances

Fisher,D.H. & Schlimmer,J.C. (1988). Concept Simplification and Predictive Accuracy. Proceedings of the Fifth International Conference on Machine Learning (pp. 22-28). Ann Arbor, Michigan: Morgan Kaufmann. - Notes why this database is highly predictable

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Examples

  data(Soybean)

Example output

Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.

partitionMap documentation built on May 2, 2019, 2:43 a.m.