71218 | ИПУ РАН

Автор(ы):

Автор(ов):

Параметры публикации

Тип публикации:

Доклад

Название:

Machine Learning. Stochastic Method of Solving the Problem of Classification with a supervised imbalanced training Set. /2022 15th International Conference Management of large-scale system development (MLSD). https://ieeexplore.ieee.org/document/9934738. Machine Learning. Stochastic Method of Solving the Problem of Classification with a supervised imbalanced training Set | IEEE Conference Publication | IEEE Xplore

Электронная публикация:

Да

DOI:

10.1109/MLSD55143.2022.9934738

Наименование конференции:

2022 15th International Conference Management of large-scale system development (MLSD)

Наименование источника:

Proceedings of the 15th International Conference Management of Large-Scale System Development (MLSD)

Город:

Москва

Издательство:

IEEE

Год издания:

2022

Страницы:

https://ieeexplore.ieee.org/document/9934738

Аннотация

In machine learning with a large class imbalance in a supervised training set, a small class can be poorly found. We use Anderson's discriminant class estimates of posterior class probabilities to solve the imbalance problem. We do not change the proportions of classes in the training set, but choose the cost of classification errors from the ranges calculated from the posterior probabilities at points in the training set of a small class. Thus, we solve the problem in the framework of the stochastic method.

Библиографическая ссылка:

Зенков В.В. Machine Learning. Stochastic Method of Solving the Problem of Classification with a supervised imbalanced training Set. /2022 15th International Conference Management of large-scale system development (MLSD). https://ieeexplore.ieee.org/document/9934738. Machine Learning. Stochastic Method of Solving the Problem of Classification with a supervised imbalanced training Set | IEEE Conference Publication | IEEE Xplore / Proceedings of the 15th International Conference Management of Large-Scale System Development (MLSD). М.: IEEE, 2022. С. https://ieeexplore.ieee.org/document/9934738.