Classical and quantum improvements of generic decision tree constructing algorithm for classification problem

Abstract

In the work, we focus on the complexity of the generic of a decision tree classifier constructing algorithm. The decision tree is constructed in ((running time in the classical case, where is a class numbers, is the input data size, is an attributes number, is a tree height. We offer two options for improving the classical version of the generic algorithm, the running time of using these options are ((general case) and ((for independent attributes). After that we suggest a quantum improvement, which uses quantum subroutines like the amplitude amplification and the Dȕrr-Høyer minimum search algorithms. The running time of the quantum algorithms is (√ () that is better than the complexity of the classical algorithm in the general case.