TESI DOCTORAL - La Salle

A.2. Data sets
Data set Number of Number of Number of Class
name objects (n) attributes (d) classes (k) imbalance
Zoo 101 17 7 40.6%–3.9%
Iris 150 4 3 33.3%–33.3%
Wine 178 13 3 39.9%–26.9%
Glass 214 10 6 35.5%–4.2%
Ionosphere 351 34 2 64.1%–35.9%
WDBC 569 32 2 62.7%–37.3%
Balance 625 4 3 46.1%–7.8%
Mfeat 2000 649 10 10%–10%
miniNG 2000 6679 20 5%–5%
Segmentation 2100 19 7 14.3%–14.3%
BBC 2225 6767 5 22.9%–17.3%
PenDigits 7494 16 10 10.4%–9.6%
Table A.2: Summary of the unimodal data sets employed in the experimental sections of
this thesis. The “Class imbalance” column presents the percentage of objects in the data
set belonging to the most and least populated categories, respectively.
9. miniNG: this is a reduced version of the 20 Newsgroups text data set, as it contains
only 2000 objects (text articles posted in Usenet) belonging to one of the 20 predefined
thematic classes (e.g. sci.electronics, rec.sport.baseball or talk.politics.mideast).
Typical text preprocessing tasks such as the removal of stop words and of terms appearing
in less than 4 documents (document frequency thresholding) gives rise to a
bag-of-words representation of each article on a 6679-dimensional tfidf -weighted (i.e.
real-valued) term space (Sebastiani, 2002).
10. Segmentation: known as the Image Segmentation data set, it contains 2100 outdoor
images regions represented by 19-dimensional real-valued feature vectors that should
be classified into one of seven texture classes: brickface, sky, foliage, cement, window,
path and grass. We have employed the test subset of the Segmentation collection.
11. BBC : this data set has been obtained from the online repository of the Machine Learning
Group of the University College Dublin (http://mlg.ucd.ie/content/view/21/). It
consists of 2225 documents from the BBC news website corresponding to stories in
five topical areas (business, entertainment, politics, sport, tech). The original documents’
representation used a 9636-dimensional term space which was reduced to 6767
real-valued attributes after removing those terms with a document frequency smaller
or equal to 4 (Sebastiani, 2002).
12. PenDigits: its original name is Pen-Based Recognition of Handwritten Digits data
set, whose training subset contains 7494 digitized handwritten digits (from 0 to 9)
captured using a pressure sensitive tablet. Each object is represented by 16 integer
attributes corresponding to the (x, y) coordinates of the electronic pen on the tablet
sampled every 100 miliseconds.
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