Unimprovable upper bounds on time complexity of decision trees
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Classification of Infinite Information Systems
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
On Compressible Information Systems
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
| Hi-index | 0.00 |
We investigate infinite information systems. Such systems are widely used in pattern recognition, data mining, discrete optimization, computational geometry. An information system is called compressible relatively to a given weight function if for each problem over the information system with sufficiently large weight (i.e., total weight of attributes in the problem description) there exists a decision tree (i) solving this problem and (ii) having the weighted depth less than the problem weight. In the paper all pairs (information system, weight function) such that the information system is compressible relatively to the weight function are described. For each such pair the behavior of Shannon type function is investigated characterizing the growth in the worst case of the minimal weighted depth of decision trees with the growth of problem weight.