TESI DOCTORAL - La Salle

A.2.1 Unimodal data sets
Appendix A. Experimental setup
A total of twelve unimodal data sets have been used in the experimental sections of this
thesis. Unless noted otherwise, these data sets have been obtained from two classic public
data repositories for the data mining and machine learning research communities such as the
UCI Knowledge Discovery in Databases Archive (Hettich and Bay, 1999) and the UCI Machine
Learning Repository (Asuncion and Newman, 1999). In the following paragraphs, we
present a brief description of each data set, summarizing their most relevant characteristics
in table A.2 as a quick reference source.
1. Zoo: the goal of this data set is to learn to classify animals into seven classes given 17
binary attributes representing features such as the presence of hair, feathers, backbone
or teeth, or whether it is an aquatic or airborne animal, among others. The number
of objects (i.e. animals) in the data set is 101.
2. Iris: a classic data set in machine learning and pattern recognition. It contains 150
objects (instances of Iris plants) represented by four real-valued features measuring
the width and length of its petals and sepals. The goal is to classify the objects into
one of the three classes of Iris plants, one of which is linearly separable from the
others, while the latter two are not linearly separable from each other.
3. Wine: this data set’s goal is to determine the origin of wines by means of chemical
analysis. It contains 178 samples of wine which must be categorized into three wine
classes based on their contents of 13 constituents such as alcohol, malic acid, or
magnesium, represented as real-valued features.
4. Glass: in this data set, 214 instances of glass are represented by 10 real-valued attributes
corresponding to their contents in chemical elements such as aluminium,
sodium, calcium, etc. The goal is to classify the objects into one of the predefined six
categories (types of glass).
5. Ionosphere: the contents of this data set are 351 radar returns from the ionosphere,
classified either as good or bad depending on whether they show evidence of some
type of structure in the ionosphere or not. Each radar return is described by 34
autocorrelation-based real-valued features.
6. WDBC : its complete name is Wisconsin Diagnostic Breast Cancer data set. It contains
569 objects (breast mass images) represented by 32 real-valued features describing
characteristics of the cell nuclei present in the image (radius, texture, perimenter,
etc.). The goal is to classify these objects into one of the possible cancer diagnostics
(malignant or benign).
7. Balance: this data set was generated to model psychological experimental results.
Each of the 625 objects is classified into three classes (as having the balance scale tip
to the right, tip to the left, or balanced). The integer-valued attributes are the left
weight, the left distance, the right weight, and the right distance.
8. Mfeat: its original name is Multiple Features data set, as it represents the objects
it contains (handwritten numerals from 0 to 9) using different real-valued features
such as Fourier coefficients, profile correlations, Karhunen-Loève coefficients, pixel
averages, Zernike moments and morphological attributes.
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