@InProceedings{10.1007/978-3-031-43789-2_14,
  author="Konstantinov, Andrei V.
  and Utkin, Lev V.
  and Lukashin, Alexey A.
  and Muliukha, Vladimir A.",
  editor="Kovalev, Sergey
  and Kotenko, Igor
  and Sukhanov, Andrey",
  title="Neural Attention Forests: Transformer-Based Forest Improvement",
  booktitle="Proceedings of the Seventh International Scientific Conference ``Intelligent Information Technologies for Industry'' (IITI'23)",
  year="2023",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="158--167",
  abstract="A new approach called NAF (the Neural Attention Forest) for solving regression and classification tasks under tabular training data is proposed. The main idea behind the proposed NAF model is to introduce the attention mechanism into the random forest by assigning attention weights calculated by neural networks of a specific form to data in leaves of decision trees and to the random forest itself in the framework of the Nadaraya-Watson kernel regression. In contrast to the available models like the attention-based random forest, the attention weights and the Nadaraya-Watson regression are represented in the form of neural networks whose weights can be regarded as trainable parameters. The first part of neural networks with shared weights is trained for all trees and computes attention weights of data in leaves. The second part aggregates outputs of the tree networks and aims to minimize the difference between the random forest prediction and the truth target value from a training set. The neural network is trained in an end-to-end manner. The combination of the random forest and neural networks implementing the attention mechanism forms a transformer for enhancing the forest predictions. Numerical experiments with real datasets illustrate the proposed method. The code implementing the approach is publicly available.",
  isbn="978-3-031-43789-2"
  }


@article{konstantinov2023new,
  title={A New Computationally Simple Approach For Implementing Neural Networks With Output Hard Constraints},
  author={Konstantinov, AV and Utkin, LV},
  journal={Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravleni{\^a}},
  volume={514},
  number={2},
  pages={80--90},
  year={2023}
}




@article{konstantinov2023flexible,
  title={Flexible deep forest classifier with multi-head attention},
  author={Konstantinov, AV and Utkin, LV and Kirpichenko, SR},
  journal={Computing, Telecommunications and Control},
  volume={16},
  number={2},
  pages={7--16},
  year={2023}
}


@article{kirpichenko2024benk,
  title={BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect},
  author={Kirpichenko, Stanislav and Utkin, Lev and Konstantinov, Andrei and Muliukha, Vladimir},
  journal={Algorithms},
  volume={17},
  number={1},
  pages={40},
  year={2024},
  publisher={MDPI}
}




@inproceedings{konstantinov2023gbmils,
  title={GBMILs: Gradient Boosting Models for Multiple Instance Learning},
  author={Konstantinov, Andrei and Utkin, Lev and Muliukha, Vladimir and Zaborovsky, Vladimir},
  booktitle={International Conference on Interactive Collaborative Robotics},
  pages={233--245},
  year={2023},
  organization={Springer}
}


@article{konstantinov_interpretable_2023,
  title={Interpretable ensembles of hyper-rectangles as base models},
  volume={35},
  issn={1433-3058},
  url={https://doi.org/10.1007/s00521-023-08929-8},
  doi={10.1007/s00521-023-08929-8},
  number={29},
  journal={Neural Computing and Applications},
  author={Konstantinov, Andrei V. and Utkin, Lev V.},
  month=oct,
  year={2023},
  pages={21771--21795}
}




@incollection{utkin2023attention,
  title={Attention-Based Random Forests and the Imprecise Pari-Mutual Model},
  author={Utkin, Lev V and Konstantinov, Andrei V and Politaeva, Natalia A},
  booktitle={Cyber-Physical Systems Engineering and Control},
  pages={3--15},
  year={2023},
  publisher={Springer}
}


@article{konstantinov2023multiple,
  title={Multiple Instance Learning with Trainable Soft Decision Tree Ensembles},
  author={Konstantinov, Andrei and Utkin, Lev and Muliukha, Vladimir},
  journal={Algorithms},
  volume={16},
  number={8},
  pages={358},
  year={2023},
  publisher={MDPI}
}




@article{utkin2023attention,
  title={Attention and self-attention in random forests},
  author={Utkin, Lev V and Konstantinov, Andrei V and Kirpichenko, Stanislav R},
  journal={Progress in Artificial Intelligence},
  pages={1--17},
  year={2023},
  publisher={Springer}
}


@article{konstantinov2021interpretable,
  title={Interpretable machine learning with an ensemble of gradient boosting machines},
  author={Konstantinov, Andrei V and Utkin, Lev V},
  journal={Knowledge-Based Systems},
  volume={222},
  pages={106993},
  year={2021},
  publisher={Elsevier}
}


@article{utkin2019weighted,
  title={A weighted random survival forest},
  author={Utkin, Lev V and Konstantinov, Andrei V and Chukanov, Viacheslav S and Kots, Mikhail V and Ryabinin, Mikhail A and Meldo, Anna A},
  journal={Knowledge-Based Systems},
  volume={177},
  pages={136--144},
  year={2019},
  publisher={Elsevier}
}


@article{kovalev2021counterfactual,
  title={Counterfactual explanation of machine learning survival models},
  author={Kovalev, Maxim and Utkin, Lev and Coolen, Frank and Konstantinov, Andrei},
  journal={Informatica},
  volume={32},
  number={4},
  pages={817--847},
  year={2021},
  publisher={Vilnius University}
}


@inproceedings{utkin2019deep,
  title={A deep forest improvement by using weighted schemes},
  author={Utkin, Lev and Konstantinov, Andrei and Meldo, Anna and Ryabinin, Mikhail and Chukanov, Viacheslav},
  booktitle={2019 24th Conference of Open Innovations Association (FRUCT)},
  pages={451--456},
  year={2019},
  organization={IEEE}
}


@article{utkin2019deep,
  title={Deep Forest as a framework for a new class of machine-learning models},
  author={Utkin, Lev V and Meldo, Anna A and Konstantinov, Andrei V},
  journal={National Science Review},
  volume={6},
  number={2},
  pages={186--187},
  year={2019},
  publisher={Oxford University Press}
}


@article{utkin2022attention,
  title={Attention-based random forest and contamination model},
  author={Utkin, Lev V and Konstantinov, Andrei V},
  journal={Neural Networks},
  volume={154},
  pages={346--359},
  year={2022},
  publisher={Elsevier}
}


@article{utkin2022ensembles,
  title={Ensembles of random SHAPs},
  author={Utkin, Lev and Konstantinov, Andrei},
  journal={Algorithms},
  volume={15},
  number={11},
  pages={431},
  year={2022},
  publisher={MDPI}
}


@article{utkin2020new,
  title={A new adaptive weighted deep forest and its modifications},
  author={Utkin, Lev V and Konstantinov, Andrei V and Chukanov, Viacheslav S and Meldo, Anna A},
  journal={International Journal of Information Technology \& Decision Making},
  volume={19},
  number={04},
  pages={963--986},
  year={2020},
  publisher={World Scientific}
}


@article{utkin2022survnam,
  title={SurvNAM: The machine learning survival model explanation},
  author={Utkin, Lev V and Satyukov, Egor D and Konstantinov, Andrei V},
  journal={Neural Networks},
  volume={147},
  pages={81--102},
  year={2022},
  publisher={Elsevier}
}


@inproceedings{konstantinov2022agboost,
  title={AGBoost: Attention-based modification of gradient boosting machine},
  author={Konstantinov, Andrei and Utkin, Lev and Kirpichenko, Stanislav},
  booktitle={2022 31st Conference of Open Innovations Association (FRUCT)},
  pages={96--101},
  year={2022},
  organization={IEEE}
}


@inproceedings{konstantinov2021gradient,
  title={Gradient boosting machine with partially randomized decision trees},
  author={Konstantinov, Andrei and Utkin, Lev and Muliukha, Vladimir},
  booktitle={2021 28th Conference of Open Innovations Association (FRUCT)},
  pages={167--173},
  year={2021},
  organization={IEEE}
}


@article{konstantinov2021generalized,
  title={A generalized stacking for implementing ensembles of gradient boosting machines},
  author={Konstantinov, Andrei V and Utkin, Lev V},
  journal={Cyber-Physical Systems: Digital Technologies and Applications},
  pages={3--16},
  year={2021},
  publisher={Springer}
}


@article{konstantinov2022multi,
  title={Multi-attention multiple instance learning},
  author={Konstantinov, Andrei V and Utkin, Lev V},
  journal={Neural Computing and Applications},
  volume={34},
  number={16},
  pages={14029--14051},
  year={2022},
  publisher={Springer}
}




@inproceedings{kots2021semi,
  title={Semi-supervised Learning for Medical Image Segmentation},
  author={Kots, Mikhail and Pozigun, Mikhail and Konstantinov, Andrei and Chukanov, Viacheslav},
  booktitle={Proceedings of International Scientific Conference on Telecommunications, Computing and Control: TELECCON 2019},
  pages={245--253},
  year={2021},
  organization={Springer}
}




@inproceedings{utkin2021combining,
  title={Combining an autoencoder and a variational autoencoder for explaining the machine learning model predictions},
  author={Utkin, Lev and Drobintsev, Pavel and Kovalev, Maxim and Konstantinov, Andrei},
  booktitle={2021 28th Conference of Open Innovations Association (FRUCT)},
  pages={489--494},
  year={2021},
  organization={IEEE}
}


@inproceedings{utkin2021deep,
  title={The Deep Survival Forest and Elastic-Net-Cox Cascade Models as Extensions of the Deep Forest},
  author={Utkin, Lev and Konstantinov, Andrei and Meldo, Anna and Sokolova, Victoria and Coolen, Frank},
  booktitle={Proceedings of International Scientific Conference on Telecommunications, Computing and Control: TELECCON 2019},
  pages={205--217},
  year={2021},
  organization={Springer}
}


@article{konstantinov2023heterogeneous,
  title={Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya--Watson Regression},
  author={Konstantinov, Andrei and Kirpichenko, Stanislav and Utkin, Lev},
  journal={Algorithms},
  volume={16},
  number={5},
  pages={226},
  year={2023},
  publisher={MDPI}
}


@article{konstantinov2023attention,
  title={Attention-like feature explanation for tabular data},
  author={Konstantinov, Andrei V and Utkin, Lev V},
  journal={International Journal of Data Science and Analytics},
  volume={16},
  number={1},
  pages={1--26},
  year={2023},
  publisher={Springer}
}


@incollection{utkin2022extension,
  title={An Extension of the Neural Additive Model for Uncertainty Explanation of Machine Learning Survival Models},
  author={Utkin, Lev and Konstantinov, Andrei},
  booktitle={Cyber-Physical Systems: Intelligent Models and Algorithms},
  pages={3--13},
  year={2022},
  publisher={Springer}
}


@article{konstantinov2021deep,
  title={Deep gradient boosting for regression problems},
  author={Konstantinov, Andrei Vladimirovich},
  journal={Информатика, телекоммуникации и управление},
  volume={14},
  number={3},
  pages={7--19},
  year={2021},
  publisher={Федеральное государственное автономное образовательное учреждение высшего~…}
}


@article{utkin2020estimation,
  title={Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors},
  author={Utkin, Lev V and Kots, Mikhail V and Chukanov, Viacheslav S and Konstantinov, Andrei V and Meldo, Anna A},
  journal={International Journal on Artificial Intelligence Tools},
  volume={29},
  number={05},
  pages={2050005},
  year={2020},
  publisher={World Scientific}
}


@inproceedings{konstantinov2023larf,
  title={LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models},
  author={Konstantinov, Andrei and Utkin, Lev and Muliukha, Vladimir},
  booktitle={Informatics},
  volume={10},
  number={2},
  pages={40},
  year={2023},
  organization={MDPI}
}






@incollection{utkin2023modifications,
  title={Modifications of SHAP for Local Explanation of Function-Valued Predictions Using the Divergence Measures},
  author={Utkin, Lev and Petrov, Artem and Konstantinov, Andrei},
  booktitle={Cyber-Physical Systems and Control II},
  pages={52--64},
  year={2023},
  publisher={Springer}
}


@incollection{utkin2023robust,
  title={Robust Models of Distance Metric Learning by Interval-Valued Training Data},
  author={Utkin, Lev and Konstantinov, Andrei and Muliukha, Vladimir and Politaeva, Natalia},
  booktitle={Cyber-Physical Systems and Control II},
  pages={65--77},
  year={2023},
  publisher={Springer}
}


@article{utkin2022improved,
  title={Improved Anomaly Detection by Using the Attention-Based Isolation Forest},
  author={Utkin, Lev and Ageev, Andrey and Konstantinov, Andrei and Muliukha, Vladimir},
  journal={Algorithms},
  volume={16},
  number={1},
  pages={19},
  year={2022},
  publisher={MDPI}
}




@inproceedings{utkin2022approach,
  title={An Approach for the Robust Machine Learning Explanation Based on Imprecise Statistical Models},
  author={Utkin, Lev and Zaborovsky, Vladimir and Muliukha, Vladimir and Konstantinov, Andrei},
  booktitle={Algorithms and Solutions Based on Computer Technology: 5th Scientific International Online Conference Algorithms and Solutions based on Computer Technology (ASBC 2021)},
  pages={127--135},
  year={2022},
  organization={Springer}
}


@inproceedings{konstantinov2022multiple,
  title={Multiple Instance Learning through Explanation by Using a Histopathology Example},
  author={Konstantinov, Andrei and Utkin, Lev},
  booktitle={2022 31st Conference of Open Innovations Association (FRUCT)},
  pages={102--108},
  year={2022},
  organization={IEEE}
}


@article{utkin2022random,
  title={Random survival forests incorporated by the Nadaraya-Watson regression},
  author={Utkin, Lev and Konstantinov, Andrei},
  journal={Информатика и автоматизация},
  volume={21},
  number={5},
  pages={851--880},
  year={2022}
}


@article{konstantinov2022random,
  title={Random Forests with Attentive Nodes},
  author={Konstantinov, Andrei V and Utkin, Lev V and Kirpichenko, Stanislav R and Kozlov, Boris V and Ageev, Andrey Y},
  journal={Procedia Computer Science},
  volume={212},
  pages={454--463},
  year={2022},
  publisher={Elsevier}
}


@article{utkin2021uncertainty,
  title={Uncertainty Interpretation of the Machine Learning Survival Model Predictions},
  author={Utkin, Lev V and Zaborovsky, Vladimir S and Kovalev, Maxim S and Konstantinov, Andrei V and Politaeva, Natalia A and Lukashin, Alexey A},
  journal={IEEE Access},
  volume={9},
  pages={120158--120175},
  year={2021},
  publisher={IEEE}
}


@inproceedings{utkin2020adaptive,
  title={An Adaptive Weighted Deep Survival Forest},
  author={Utkin, Lev V and Konstantinov, Andrei and Lukashin, Aleksey A and Muliukha, Vladimir A},
  booktitle={2020 XXIII International Conference on Soft Computing and Measurements (SCM)},
  pages={198--201},
  year={2020},
  organization={IEEE}
}


@inproceedings{уткин2020адаптивный,
  title={Адаптивный весовой глубокий лес выживаемости},
  author={Уткин, ЛВ and Константинов, АВ and Лукашин, АА and Мулюха, ВА},
  booktitle={Международная конференция по мягким вычислениям и измерениям},
  volume={1},
  pages={220--223},
  year={2020},
  organization={Федеральное государственное автономное образовательное учреждение высшего~…}
}


@inproceedings{вердина2017алгоритмы,
  title={Алгоритмы фиксации уровня и быстрого распространения контура для полуавтоматической сегментации медицинских изображений},
  author={Вердина, МК and Константинов, АВ and Позигун, МВ and Чуканов, ВС},
  booktitle={Неделя науки СПбПУ},
  pages={78--81},
  year={2017}
}


@inproceedings{константинов2018сегментация,
  title={СЕГМЕНТАЦИЯ ТРЁХМЕРНЫХ МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ АЛГОРИТМОВ КЛАССИФИКАЦИИ},
  author={Константинов, АВ and Чуканов, ВС},
  booktitle={Неделя науки СПбПУ},
  pages={187--190},
  year={2018}
}