anytime algorithms for learning anytime classifiers saher ... - Technion

Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008
17. TAE: The Teaching Assistant Evaluation dataset, taken from the UCI
Repository, consists of evaluations of teaching performance; scores are
”low”, ”medium”, or ”high”.
18. Tic-Tac-Toe: The problem, taken from the UCI Repository, deals with
the classification of legal tic-tac-toe end games, as wins or non-wins for
the x player. Each example is represented by 9 nominal attributes, which
represent the slot values.
19. Thyroid: In this dataset, taken from the UCI repository, the class is diagnostic
(normal or hyperthyroid). The features represent medical information
and basic characteristics of the patient.
20. Voting: This dataset, taken from the UCI repository, includes votes for each
member of the U.S. House of Representatives on the 16 key votes identified
by the CQA. The class of each record is Democrat or Republican.
21. Wine: This problem, taken from the UCI Repository, deals with the classification
of wines into 3 class types. Each example is represented by 13
continuous attributes, which represent measures of chemical elements in
the wine.
22. Zoo: In this domain, taken from the UCI Repository, the goal is to determine
the type of the animal from several attributes.
23. Multiplexer: The multiplexer task was used by several researchers for evaluating
classifiers, e.g., Quinlan (1993). An instance is a series of bits of
length a + 2 a , where a is a positive integer. The first a bits represent an
index into the remaining bits and the label of the instance is the value of the
indexed bit. In our experiments we considered the 20-Multiplexer (a = 4).
The dataset contains 500 randomly drawn instances.
24. Boolean XOR: Parity-like functions are known to be problematic for many
learning algorithms. However, they naturally arise in real-world data, such
as the Drosophila survival concept (Page & Ray, 2003). We considered
XOR of five and ten variables with additional irrelevant attributes.
25. Numeric XOR: A XOR based numeric dataset that has been used to evaluate
learning algorithms, e.g., Baram, El-Yaniv, and Luz (2003). Each
example consists of values for x and y coordinates. The example is labeled
1 if the product of x and y is positive, and −1 otherwise. We generalized
this domain for three and four dimensions and added irrelevant variables to
make the concept harder.
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