title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¸Ð·Ð¾Ð»Ð¸ÑÑÑÑÐµÐ³Ð¾ Ð»ÐµÑÐ° Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹ Ð² ÑÑÐ°Ð½Ð·Ð°ÐºÑÐ¸Ð¾Ð½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development and research of isolating forest algorithms for anomaly detection in transactional data
Creators: name: Ð¤Ð¸Ð»Ð¸Ð¿Ð¿Ð¾Ð²Ð°, ÐÐ»ÐµÐ½Ð°
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: 2022
Subjects: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
Ð¸Ð·Ð¾Ð»Ð¸ÑÑÑÑÐ¸Ð¹ Ð»ÐµÑ
Ð¾ÑÐµÐ½ÐºÐ° Ð¿Ð»Ð¾ÑÐ½Ð¾ÑÑÐ¸ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ
Ð²ÐµÑÐ¾Ð²ÑÐµ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½ÑÑ
anomaly detection
isolation forest
desity estimation
weighting coefficients

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description: The subject of the study is the modification of the existing the isolating forest algorithm (hereinafter â IF), and the goal is to increase the efficiency of anomaly detection via the isolating forest algorithm by modifying it. Methods of mathematical statistics, machine learning and object-oriented programming were used in the work. The IF algorithm and its modifications were investigated: expanded IF, IF of seismic activity, generalized IF. A proprietary modification of the IF, a weight insulating forest, was also proposed and studied. The algorithms were implemented in C++ 20 without using third-party libraries. The data set for testing contained 16 million transactions collected over approximately 5 months of operation. The developed and implemented model of the weight isolating forest during testing of anomaly detection on depersonalized transactional data proved to be the most balanced IF model. Identification of the range of parameters of the number of isolating trees and the sample size allows to achieve greater accuracy than other modifications of the IF: models of extended IF and IF seismic activity.