A general formal approach for fuzzy reliability analysis in the possibility context. - Lev V. Utkin; Sergey V. Gurov (Fuzzy Sets Syst., 1996)

Steady-state reliability of repairable systems by combined probability and possibility assumptions. - Lev V. Utkin; Sergey V. Gurov (Fuzzy Sets Syst., 1998)

Imprecise Reliability Models for the General Lifetime Distribution Classes. - Lev V. Utkin; Sergey V. Gurov (ISIPTA, 1999)

Imprecise Reliability of General Structures. - Lev V. Utkin; Sergey V. Gurov (Knowl. Inf. Syst., 1999)

Different faces of the natural extension. - Igor Kozine; Lev V. Utkin (ISIPTA, 2001)

Computing the reliability of complex systems. - Igor Kozine; Lev V. Utkin (ISIPTA, 2001)

Stress-strength reliability models under incomplete information. - Lev V. Utkin; Igor Kozine (Int. J. Gen. Syst., 2002)

Interval-Valued Finite Markov Chains. - Igor Kozine; Lev V. Utkin (Reliab. Comput., 2002)

A Second-Order Uncertainty Model of Independent Random Variables: An Example of the Stress-Strength Reliability. - Lev V. Utkin (ISIPTA, 2003)

Decision Making with Imprecise Second-Order Probabilities. - Lev V. Utkin (ISIPTA, 2003)

Imprecise Second-Order Hierarchical Uncertainty Model . - Lev V. Utkin (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2003)

A second-order uncertainty model for calculation of the interval system reliability. - Lev V. Utkin (Reliab. Eng. Syst. Saf., 2003)

Reliability models of m-out-of-n systems under incomplete information. - Lev V. Utkin (Comput. Oper. Res., 2004)

Interval reliability of typical systems with partially known probabilities. - Lev V. Utkin (Eur. J. Oper. Res., 2004)

An approach to combining unreliable pieces of evidence and their propagation in a system response analysis. - Igor Kozine; Lev V. Utkin (Reliab. Eng. Syst. Saf., 2004)

A new efficient algorithm for computing the imprecise reliability of monotone systems. - Lev V. Utkin (Reliab. Eng. Syst. Saf., 2004)

Powerful algorithms for decision making under partial prior information and general ambiguity attitudes. - Lev V. Utkin; Thomas Augustin 001 (ISIPTA, 2005)

Comments on the paper "A behavioural model for vague probability assessments" by Gert de Cooman. - Lev V. Utkin (Fuzzy Sets Syst., 2005)

Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model. - Lev V. Utkin (Fuzzy Sets Syst., 2005)

Constructing imprecise probability distributions. - Igor Kozine; Lev V. Utkin (Int. J. Gen. Syst., 2005)

Imprecise second-order model for a system of independent random variables. - Lev V. Utkin (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2005)

Computing System Reliability Given Interval-Valued Characteristics of the Components. - Lev V. Utkin; Igor Kozine (Reliab. Comput., 2005)

Ranking procedures by pairwise comparison using random sets and the imprecise Dirichlet model. - Lev V. Utkin (Appl. Math. Comput., 2006)

A method for processing the unreliable expert judgments about parameters of probability distributions. - Lev V. Utkin (Eur. J. Oper. Res., 2006)

Cautious Analysis of Project Risks by Interval-Valued Initial Data. - Lev V. Utkin (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2006)

Second-order uncertainty calculations by using the imprecise Dirichlet model. - Lev V. Utkin (Intell. Data Anal., 2007)

Decision making under incomplete data using the imprecise Dirichlet model. - Lev V. Utkin; Thomas Augustin 001 (Int. J. Approx. Reason., 2007)

Risk Analysis under Partial Prior Information and Nonmonotone Utility Functions. - Lev V. Utkin (Int. J. Inf. Technol. Decis. Mak., 2007)

Imprecise reliability: An introductory overview. - Lev V. Utkin; Frank P. A. Coolen (Intelligence in Reliability Engineering, 2007)

A new ranking procedure by incomplete pairwise comparisons using preference subsets. - Lev V. Utkin (Intell. Data Anal., 2009)

Computing expectations with continuous p-boxes: Univariate case. - Lev V. Utkin; Sébastien Destercke (Int. J. Approx. Reason., 2009)

Regression analysis using the imprecise Bayesian normal model. - Lev V. Utkin (Int. J. Data Anal. Tech. Strateg., 2010)

Imprecise Reliability. - Frank P. A. Coolen; Lev V. Utkin (International Encyclopedia of Statistical Science, 2011)

A Pessimistic Approach for Solving a Multi-criteria Decision Making. - Van Hieu Nguyen; Lev V. Utkin; Dang Duy Thang (KSE, 2012)

Fuzzy One-Class Classification Model Using Contamination Neighborhoods. - Lev V. Utkin (Adv. Fuzzy Syst., 2012)

Combining of judgments in imprecise voting multi-criteria decision problems. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Appl. Decis. Sci., 2012)

A machine learning algorithm for classification under extremely scarce information. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Data Anal. Tech. Strateg., 2012)

The DS/AHP Method under Partial Information about Criteria and Alternatives by Several Levels of Criteria. - Lev V. Utkin; Natalia V. Simanova (Int. J. Inf. Technol. Decis. Mak., 2012)

Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. - Lev V. Utkin; Yulia A. Zhuk (Adv. Artif. Intell., 2013)

Robust novelty detection in the framework of a contamination neighbourhood. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Intell. Inf. Database Syst., 2013)

Fuzzy decision making using the imprecise Dirichlet model. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Math. Oper. Res., 2013)

An Imprecise Boosting-like Approach to Classification. - Lev V. Utkin (Int. J. Pattern Recognit. Artif. Intell., 2013)

A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy. - Lev V. Utkin; Yulia A. Zhuk; Anatoly I. Chekh (MLDM, 2014)

Imprecise prior knowledge incorporating into one-class classification. - Lev V. Utkin; Yulia A. Zhuk (Knowl. Inf. Syst., 2014)

Robust boosting classification models with local sets of probability distributions. - Lev V. Utkin; Yulia A. Zhuk (Knowl. Based Syst., 2014)

A framework for imprecise robust one-class classification models. - Lev V. Utkin (Int. J. Mach. Learn. Cybern., 2014)

Robust Classifiers Using Imprecise Probability Models and Importance of Classes. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Artif. Intell. Tools, 2015)

The imprecise Dirichlet model as a basis for a new boosting classification algorithm. - Lev V. Utkin (Neurocomputing, 2015)

Improving over-fitting in ensemble regression by imprecise probabilities. - Lev V. Utkin; Andrea Wiencierz (Inf. Sci., 2015)

A new robust model of one-class classification by interval-valued training data using the triangular kernel. - Lev V. Utkin; Anatoly I. Chekh (Neural Networks, 2015)

Imprecise inference for warranty contract analysis. - Lev V. Utkin; Frank P. A. Coolen; Sergey V. Gurov (Reliab. Eng. Syst. Saf., 2015)

Detection of anomalous behavior in a robot system based on deep learning elements. - Lev V. Utkin; V. S. Zaborovskii; Sergey G. Popov (Autom. Control. Comput. Sci., 2016)

Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels. - Lev V. Utkin; Anatoly I. Chekh; Yulia A. Zhuk (Neural Networks, 2016)

Reliability of repairable reserved systems with failure aftereffect. - Sergey V. Gurov; Lev V. Utkin (Autom. Remote. Control., 2017)

Siamese neural network for intelligent information security control in multi-robot systems. - Lev V. Utkin; Vladimir S. Zaborovsky; Sergey G. Popov (Autom. Control. Comput. Sci., 2017)

Interval SVM-Based Classification Algorithm Using the Uncertainty Trick. - Lev V. Utkin; Yulia A. Zhuk (Int. J. Artif. Intell. Tools, 2017)

An one-class classification support vector machine model by interval-valued training data. - Lev V. Utkin; Yulia A. Zhuk (Knowl. Based Syst., 2017)

A modification of the Lasso method by using the Bahadur representation for the genome-wide association study. - Lev V. Utkin; Yulia A. Zhuk (Informatica (Slovenia), 2018)

A Siamese Deep Forest. - Lev V. Utkin; Mikhail A. Ryabinin (Knowl. Based Syst., 2018)

A robust weighted SVR-based software reliability growth model. - Lev V. Utkin; Frank P. A. Coolen (Reliab. Eng. Syst. Saf., 2018)

Database Acquisition for the Lung Cancer Computer Aided Diagnostic Systems. - Anna A. Meldo; Lev V. Utkin; Alexey Lukashin; Vladimir Muliukha; Vladimir S. Zaborovsky (FRUCT, 2019)

A Deep Forest Improvement by Using Weighted Schemes. - Lev V. Utkin; Andrei V. Konstantinov; Anna A. Meldo; Mikhail A. Ryabinin; Viacheslav S. Chukanov (FRUCT, 2019)

An Ensemble of Triplet Neural Networks for Differential Diagnostics of Lung Cancer. - Lev V. Utkin; Anna A. Meldo; Maxim Kovalev; Ernest M. Kasimov (FRUCT, 2019)

Imprecise Extensions of Random Forests and Random Survival Forests. - Lev V. Utkin; Maxim Kovalev; Anna A. Meldo; Frank P. A. Coolen (ISIPTA, 2019)

An Adaptive Weighted Deep Forest Classifier. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Mikhail V. Kots; Anna A. Meldo (CoRR, 2019)

Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. - Lev V. Utkin; Mikhail V. Kots; Viacheslav S. Chukanov (CoRR, 2019)

An explanation method for Siamese neural networks. - Lev V. Utkin; Maxim S. Kovalev; Ernest M. Kasimov (CoRR, 2019)

Discriminative Metric Learning with Deep Forest. - Lev V. Utkin; Mikhail A. Ryabinin (Int. J. Artif. Intell. Tools, 2019)

An imprecise extension of SVM-based machine learning models. - Lev V. Utkin (Neurocomputing, 2019)

A weighted random survival forest. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Mikhail V. Kots; Mikhail A. Ryabinin; Anna A. Meldo (Knowl. Based Syst., 2019)

A deep forest classifier with weights of class probability distribution subsets. - Lev V. Utkin; Maxim Kovalev; Anna A. Meldo (Knowl. Based Syst., 2019)

Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System. - Vladimir Muliukha; Alexey Lukashin; Lev V. Utkin; Mikhail Popov; Anna A. Meldo (ESANN, 2020)

The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. - Anna A. Meldo; Lev V. Utkin; Maxim Kovalev; Ernest M. Kasimov (Artif. Intell. Medicine, 2020)

Imprecise weighted extensions of random forests for classification and regression. - Lev V. Utkin; Maxim S. Kovalev; Frank P. A. Coolen (Appl. Soft Comput., 2020)

Explanation of Siamese Neural Networks for Weakly Supervised Learning. - Lev V. Utkin; Maxim Kovalev; Ernest M. Kasimov (Comput. Informatics, 2020)

SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models. - Lev V. Utkin; Maxim S. Kovalev; Ernest M. Kasimov (CoRR, 2020)

Gradient boosting machine with partially randomized decision trees. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2020)

Counterfactual explanation of machine learning survival models. - Maxim S. Kovalev; Lev V. Utkin (CoRR, 2020)

A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2020)

An imprecise deep forest for classification. - Lev V. Utkin (Expert Syst. Appl., 2020)

Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. - Lev V. Utkin; Mikhail V. Kots; Viacheslav S. Chukanov; Andrei V. Konstantinov; Anna A. Meldo (Int. J. Artif. Intell. Tools, 2020)

A New Adaptive Weighted Deep Forest and Its Modifications. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Anna A. Meldo (Int. J. Inf. Technol. Decis. Mak., 2020)

Improvement of the Deep Forest Classifier by a Set of Neural Networks. - Lev V. Utkin; Kirill D. Zhuk (Informatica (Slovenia), 2020)

SurvLIME: A method for explaining machine learning survival models. - Maxim S. Kovalev; Lev V. Utkin; Ernest M. Kasimov (Knowl. Based Syst., 2020)

A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. - Maxim S. Kovalev; Lev V. Utkin (Neural Networks, 2020)

Gradient Boosting Machine with Partially Randomized Decision Trees. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (FRUCT, 2021)

Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions. - Lev V. Utkin; Pavel D. Drobintsev; Maxim Kovalev; Andrei V. Konstantinov (FRUCT, 2021)

Uncertainty Interpretation of the Machine Learning Survival Model Predictions. - Lev V. Utkin; Vladimir S. Zaborovsky; Maxim S. Kovalev; Andrei V. Konstantinov; Natalia A. Politaeva; Alexey Lukashin (IEEE Access, 2021)

An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data. - Lev V. Utkin; Andrei V. Konstantinov; Kirill A. Vishniakov (CoRR, 2021)

Counterfactual Explanation of Machine Learning Survival Models. - Maxim Kovalev; Lev V. Utkin; Frank P. A. Coolen; Andrei V. Konstantinov (Informatica, 2021)

Interpretable machine learning with an ensemble of gradient boosting machines. - Andrei V. Konstantinov; Lev V. Utkin (Knowl. Based Syst., 2021)

Multiple Instance Learning through Explanation by Using a Histopathology Example. - Andrei V. Konstantinov; Lev V. Utkin (FRUCT, 2022)

AGBoost: Attention-based Modification of Gradient Boosting Machine. - Andrei V. Konstantinov; Lev V. Utkin; Stanislav Kirpichenko (FRUCT, 2022)

Ensembles of Random SHAPs. - Lev V. Utkin; Andrei V. Konstantinov (Algorithms, 2022)

Attention and Self-Attention in Random Forests. - Lev V. Utkin; Andrei V. Konstantinov (CoRR, 2022)

Improved Anomaly Detection by Using the Attention-Based Isolation Forest. - Lev V. Utkin; Andrey Y. Ageev; Andrei V. Konstantinov (CoRR, 2022)

LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2022)

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. - Stanislav R. Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov (CoRR, 2022)

Multi-attention multiple instance learning. - Andrei V. Konstantinov; Lev V. Utkin (Neural Comput. Appl., 2022)

Attention-based random forest and contamination model. - Lev V. Utkin; Andrei V. Konstantinov (Neural Networks, 2022)

SurvNAM: The machine learning survival model explanation. - Lev V. Utkin; Egor D. Satyukov; Andrei V. Konstantinov (Neural Networks, 2022)

GBMILs: Gradient Boosting Models for Multiple Instance Learning. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha; Vladimir S. Zaborovsky (ICR, 2023)

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. - Andrei V. Konstantinov; Stanislav Kirpichenko; Lev V. Utkin (Algorithms, 2023)

Multiple Instance Learning with Trainable Soft Decision Tree Ensembles. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (Algorithms, 2023)

Multiple Instance Learning with Trainable Decision Tree Ensembles. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2023)

Neural Attention Forests: Transformer-Based Forest Improvement. - Andrei V. Konstantinov; Lev V. Utkin; Alexey Lukashin; Vladimir Muliukha (CoRR, 2023)

A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2023)

SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov (CoRR, 2023)

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov (CoRR, 2023)

LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (Informatics, 2023)

Improved Anomaly Detection by Using the Attention-Based Isolation Forest. - Lev V. Utkin; Andrey Y. Ageev; Andrei V. Konstantinov; Vladimir Muliukha (Algorithms, 2023)

Attention-like feature explanation for tabular data. - Andrei V. Konstantinov; Lev V. Utkin (Int. J. Data Sci. Anal., 2023)

Attention and self-attention in random forests. - Lev V. Utkin; Andrei V. Konstantinov; Stanislav R. Kirpichenko (Prog. Artif. Intell., 2023)

Interpretable ensembles of hyper-rectangles as base models. - Andrei V. Konstantinov; Lev V. Utkin (Neural Comput. Appl., 2023)

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. - Stanislav Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov; Vladimir Muliukha (Algorithms, 2024)

Dual feature-based and example-based explanation methods. - Andrei V. Konstantinov; Boris V. Kozlov; Stanislav R. Kirpichenko; Lev V. Utkin (CoRR, 2024)

Generating Survival Interpretable Trajectories and Data. - Andrei V. Konstantinov; Stanislav R. Kirpichenko; Lev V. Utkin (CoRR, 2024)

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2024)

Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images. - Rinat I. Dumaev; Sergei A. Molodyakov; Lev V. Utkin (CoRR, 2024)

FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules. - Lev V. Utkin; Andrei V. Konstantinov; Stanislav R. Kirpichenko (CoRR, 2024)

SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms. - Lev V. Utkin; Semen P. Khomets; Vlada A. Efremenko; Andrei V. Konstantinov (CoRR, 2024)

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov; Vladimir Mulukha (IEEE Access, 2025)

Dual feature-based and example-based explanation methods. - Andrei V. Konstantinov; Boris V. Kozlov; Stanislav Kirpichenko; Lev V. Utkin; Vladimir Muliukha (Frontiers Artif. Intell., 2025)

Survival Concept-Based Learning Models. - Stanislav R. Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov; Natalya M. Verbova (CoRR, 2025)

A novel gradient-based method for decision trees optimizing arbitrary differential loss functions. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2025)

Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges. - Valerii A. Zuev; Elena G. Salmagambetova; Stepan N. Djakov; Lev V. Utkin (CoRR, 2025)

Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions. - Lev V. Utkin; Semen P. Khomets; Vlada A. Efremenko; Andrei V. Konstantinov; Natalya M. Verbova (CoRR, 2025)

Survival Analysis as Imprecise Classification with Trainable Kernels. - Andrei V. Konstantinov; Vlada A. Efremenko; Lev V. Utkin (CoRR, 2025)