Monotone Decision Trees and Noisy Data
The decision tree algorithm for monotone classification presented in [4, 10] requires strictly monotone data sets. This paper addresses the problem of noise due to violation of the monotonicity constraints and proposes a modification of the algorithm to handle noisy data. It also presents methods for controlling the size of the resulting trees while keeping the monotonicity property whether the data set is monotone or not.
|Keywords||monotone decision trees, noise, ordinal classification, pruning|
|Publisher||Erasmus Research Institute of Management (ERIM)|
Bioch, J.C., & Popova, V.. (2002). Monotone Decision Trees and Noisy Data (No. ERS-2002-53-LIS). Erasmus Research Institute of Management (ERIM). Retrieved from http://hdl.handle.net/1765/207