Time and space complexity of deterministic and nondeterministic decision trees

Abstract In this paper, we study arbitrary infinite binary information systems each of which consists an set called universe and two-valued functions (attributes) defined on the universe. We consider notion a problem over system, is described by finite number attributes mapping associating decision to tuple attribute values. As algorithms for solving, use deterministic nondeterministic trees. time space complexity, depth nodes in worst case, with growth description, (i) minimum trees grows either almost as logarithm or linearly, (ii) bounded from above constant (iii) has polynomial exponential growth, (iv) growth. Based these results, divide all into five complexity classes, class issues related time-space trade-off