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

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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 7. Corral: An artificial dataset first used by John, Kohavi, and Pfleger (1994). 8. Glass: In this domain, taken from the UCI Repository, the goal is to determine the type of glass from its characteristics. 9. Heart: In this domain, taken from the UCI Repository, the goal field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. 10. Hepatitis: In this problem, taken from the UCI Repository, the diagnosis is known, and the problem is to determine the likely outcome of the disease. 11. Iris: This dataset is taken from the UCI Repository and contains 3 classes of 50 instances each, where each class refers to a type of iris plant. 12. Monks problems: This set, taken from the UCI Repository, contains three problems. Each example is represented by 5 nominal attributes in the range 1, 2, 3, 4. The problems are: • Monks-1: (a1 = a2)or(a5 = 1). • Monks-2: exactly two of (a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1). • Monks-3: ((a5 = 3)and(a4 = 1))or((a5 �= 4)and(a2 �= 3)), with an added 5% class noise. The original datasets are already partitioned into training and testing sets. 13. Mushroom: This dataset, taken from the UCI Repository, includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota family. Each species is identified as edible or poisonous. 14. Nursery: This database, taken from the UCI Repository, was derived from a hierarchical decision model originally developed to rank applications for nursery schools. 15. Pima: The Pima Indians Diabetes dataset, taken from the UCI Repository, includes several medical tests and the possible classes are either the patient is healthy or the patient has diabetes. 16. Solar Flare: This problem is taken from the UCI Repository. Each instance represents captured features for one active region on the sun. Among the three possible classes, we considered the C-class flare where the instances are more distributed. 140
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. 141
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