title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð´Ð½Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸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: Research of the efficiency of one-class classification and PU-learning algorithms
Creators: name: ÐÑÑÐ°ÑÐµÐµÐ²Ð°, ÐÑÐ±Ð¾Ð²Ñ
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: 2020
Subjects: Ð¾Ð´Ð½Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ ÑÐ°ÑÑÐ¸ÑÐ½ÑÐ¼ Ð¿ÑÐ¸Ð²Ð»ÐµÑÐµÐ½Ð¸ÐµÐ¼ ÑÑÐ¸ÑÐµÐ»Ñ
Ð¾ne class classification
PU-learning
machine learning
semi-supervised learning

description: Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð´Ð½Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (Ð°Ð½Ð³Ð». Positive Unlabeled learning) Ð½Ð° ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¸Ð¿Ð°Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð°Ñ Ðº ÑÐ°Ð·ÑÐ¼ÐµÐ½Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ÑÑÐ¾Ð´Ð½ÑÑ ÐºÐ»Ð°ÑÑÐ¾Ð². ÐÑÐ» Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÐµÐºÑÑÐµÐ³Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² ÑÑÐ¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÐµÐ¼ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð². ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÐºÑÑÐ¸Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ ÑÐ¿ÐµÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð´Ð»Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ. ÐÐ»Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Difference of Estimated Densities based Positive-Unlabeled Learning (DEDPUL) Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð´Ð½Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Deep Support Vector Data Description (Deep SVDD). ÐÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¾ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ, Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÐµÐ³Ð¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐ´ÐµÐ»Ð°Ð½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð²ÑÐ²Ð¾Ð´Ñ. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð² ÑÐ»ÑÑÐ°ÑÑ, ÐºÐ¾Ð³Ð´Ð° ÑÐ°Ð·Ð¼ÐµÑ Ð¸Ð·Ð²ÐµÑÑÐ½Ð¾Ð¹ Ð¿Ð¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐ¸ Ð¼Ð°Ð» Ð¸Ð»Ð¸ Ð² Ð½ÐµÐ¹ ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÑÑ Ð½ÐµÐºÐ¾ÑÑÐµÐºÑÐ½Ð¾ ÑÐ°Ð·Ð¼ÐµÑÐµÐ½Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ, ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÐµÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ»ÑÑÐ°ÑÑ ÑÐ»Ð¾Ð¶Ð½Ð¾Ð³Ð¾ ÑÐ°Ð·ÑÐ¼ÐµÐ½Ð¾Ð²Ð°Ð½Ð¸Ñ, Ñ ÑÑÐµÑÐ¾Ð¼ Ð²ÑÐ¾Ð´ÑÑÐ¸Ñ Ð² Ð¾ÑÑÐ¸ÑÐ°ÑÐµÐ»ÑÐ½ÑÐµ Ð¸ Ð¿Ð¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½ÑÐµ ÐºÐ»Ð°ÑÑÑ Ð¿Ð¾Ð´ÐºÐ»Ð°ÑÑÐ¾Ð², Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ ÑÑÐ°Ð²Ð½Ð¸Ð¼ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ. ÐÑÐ¸ ÑÐ°Ð±Ð¾ÑÐµ Ñ ÑÐµÐºÑÑÐ¾Ð²ÑÐ¼Ð¸ Ð¸ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÐ¼Ð¸ ÑÐ¸ÑÐ»Ð¾Ð²ÑÐ¼Ð¸ Ð´Ð°Ð½Ð½ÑÐ¼Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð» Ð»ÑÑÑÐ¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ. ÐÑÐ¸ ÑÐ°Ð±Ð¾ÑÐµ Ñ Ð½Ð°Ð±Ð¾ÑÐ°Ð¼Ð¸ Ð´Ð°Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼ÐµÑÑÐ¸Ðº ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð´Ð»Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÑÐ¾Ð¶Ð¸. Ð¢Ð°ÐºÐ¶Ðµ Ð´Ð»Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð° ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ°Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐ¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð´Ð½Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ PU-Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ¸Ð²ÐµÐ´ÐµÐ½Ñ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ¹ ÑÐ°Ð±Ð¾ÑÑ.
description: The given work compares the effectiveness of the algorithms of one-class classification and PU-learning (eng. Positive Unlabeled learning) on different types of data and with different approaches to dereferencing of the original classes. A review of the current state of research in this area and an analysis of the compared methods were performed. Solutions were proposed for the current problems of comparing algorithms. Specific metrics for experimental comparison were analyzed and selected. For further research, the Difference of Estimated Densities based Positive-Unlabeled Learning (DEDPUL) PU-learning algorithm and the Deep Support Vector Data Description (Deep SVDD) one-class classification algorithm are implemented. Preformed an experimental study, results were processed and analyzed. According to the results of the study, the following conclusions were made. Shown that in cases where the size of the known positive sample is small or it contains incorrectly labeled data, it is more efficient to use the PU-learning algorithm. In cases of complex dereferencing, taking into account the subclasses that are part of the negative and positive classes, the algorithms show comparable efficiency. When working with textual and multidimensional numeric data, the PU-learning algorithm has shown advantages in efficiency. When working with image data sets, the results of the efficiency metrics for the algorithms are similar. Also, for images the theoretical probability of improving the overall results of the problem using a combination of classmate classification algorithms and PU learning is shown.