title: ÐÐ¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´ÐµÑÐµÐ²ÑÐµÐ² ÑÐµÑÐµÐ½Ð¸Ð¹ Ð¸ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð»ÐµÑÐ° Ð´Ð»Ñ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÑÐ¾Ð±Ð°ÑÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð²Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ ÑÑÐ¼Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Modification of decision trees and random forest models to ensure robustness and accuracy of classification based on the introduction of random noise
Creators: name: ÐÐ»Ð¸Ð·Ð°ÑÐ¾Ð²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð´ÐµÑÐµÐ²ÑÑ ÑÐµÑÐµÐ½Ð¸Ð¹
CART
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Ð¿ÐµÑÐµÐºÑÑÑÐ¸Ðµ ÐºÐ»Ð°ÑÑÐ¾Ð²
Ð²Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ ÑÑÐ¼Ð°
machine learning
classification
decision tree
random forest
class overlapping
random noise addition

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´ÐµÑÐµÐ²ÑÐµÐ² ÑÐµÑÐµÐ½Ð¸Ð¹ CART Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð¾Ð¹ Ð¿ÐµÑÐµÐºÑÑÑÐ¸Ñ ÐºÐ»Ð°ÑÑÐ¾Ð². Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð¾ ÐµÐ³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð² Ð°Ð½ÑÐ°Ð¼Ð±Ð»ÐµÐ²Ð¾Ð¼ Ð¼ÐµÑÐ¾Ð´Ðµ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð»ÐµÑÐ°. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ ÑÐ°Ð±Ð¾ÑÑ Ð¾ÑÐ¸Ð³Ð¸Ð½Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° CART, ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð²Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ ÑÑÐ¼Ð° Ð½Ð° ÑÑÐ°Ð¿Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾, ÐºÐ¾Ð¿Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐµÐ½Ð¸ÑÐ¾Ð²Ð¾ÑÐ½ÑÑ Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ð¹ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð±Ð»Ð¸Ð·ÐºÐ¸Ñ Ðº ÑÐ°Ð·Ð´ÐµÐ»ÑÑÑÐµÐ¹ Ð³Ð¸Ð¿ÐµÑÐ¿Ð»Ð¾ÑÐºÐ¾ÑÑÐ¸. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾, ÑÑÐ¾ ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÐ¹ Ð½Ð° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ðµ CART, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿Ð¾Ð»ÑÑÐ°ÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½ÑÐµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ Ñ Ð¿ÐµÑÐµÑÐµÑÐµÐ½Ð¸ÐµÐ¼ ÐºÐ»Ð°ÑÑÐ¾Ð². ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ð½Ð° Ð¾ÑÐºÑÑÑÑÑ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ Ð¸Ð· ÑÑÐµÑÑ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½Ñ.
description: This paper is devoted to the development of modification of the CART decision tree algorithm for solving the classification tasks on data with class overlap problem. The developed modification has been applied as a part of a Random Forest. The specifics of the original CART algorithm were investigated. Modification based on introduction of random noise during training of the algorithm, namely, copying the training objects closest to the separating hyperplane was developed and implemented. The experimental study showed that the random forest based on the modified CART algorithm produces more accurate predictions on data with overlapping classes. The quality of the algorithm was investigated on public datasets mainly from the medical field.