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ORCIDLev V. Utkin
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- affiliation: Peter the Great St. Petersburg Polytechnic University, Russia
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2020 – today
- 2024
[j78]Stanislav Kirpichenko
, Lev V. Utkin, Andrei V. Konstantinov, Vladimir Muliukha:
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. Algorithms 17(1): 40 (2024)- [i36]Andrei V. Konstantinov, Boris V. Kozlov, Stanislav R. Kirpichenko, Lev V. Utkin:
Dual feature-based and example-based explanation methods. CoRR abs/2401.16294 (2024) - [i35]Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin:
Generating Survival Interpretable Trajectories and Data. CoRR abs/2402.12331 (2024) - [i34]Andrei V. Konstantinov, Lev V. Utkin:
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning. CoRR abs/2402.14726 (2024) - [i33]Rinat I. Dumaev, Sergei A. Molodyakov, Lev V. Utkin:
Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images. CoRR abs/2405.17483 (2024) - [i32]Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko:
FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules. CoRR abs/2406.19897 (2024) - 2023
- [j77]Lev V. Utkin, Andrey Y. Ageev, Andrei V. Konstantinov, Vladimir Muliukha:
Improved Anomaly Detection by Using the Attention-Based Isolation Forest. Algorithms 16(1): 19 (2023) - [j76]Andrei V. Konstantinov, Stanislav Kirpichenko, Lev V. Utkin:
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. Algorithms 16(5): 226 (2023) - [j75]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
Multiple Instance Learning with Trainable Soft Decision Tree Ensembles. Algorithms 16(8): 358 (2023) - [j74]Andrei V. Konstantinov, Lev V. Utkin:
Attention-like feature explanation for tabular data. Int. J. Data Sci. Anal. 16(1): 1-26 (2023) - [j73]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models. Informatics 10(2): 40 (2023) - [j72]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable ensembles of hyper-rectangles as base models. Neural Comput. Appl. 35(29): 21771-21795 (2023) - [j71]Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko:
Attention and self-attention in random forests. Prog. Artif. Intell. 12(3): 257-273 (2023) - [c19]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha, Vladimir S. Zaborovsky:
GBMILs: Gradient Boosting Models for Multiple Instance Learning. ICR 2023: 233-245 - [i31]Andrei V. Konstantinov, Lev V. Utkin:
Multiple Instance Learning with Trainable Decision Tree Ensembles. CoRR abs/2302.06601 (2023) - [i30]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable Ensembles of Hyper-Rectangles as Base Models. CoRR abs/2303.08625 (2023) - [i29]Andrei V. Konstantinov, Lev V. Utkin, Alexey Lukashin, Vladimir Muliukha:
Neural Attention Forests: Transformer-Based Forest Improvement. CoRR abs/2304.05980 (2023) - [i28]Andrei V. Konstantinov, Lev V. Utkin:
A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. CoRR abs/2307.10459 (2023) - [i27]Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov:
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator. CoRR abs/2308.03730 (2023) - [i26]Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov:
SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models. CoRR abs/2312.06638 (2023) - 2022
- [j70]Lev V. Utkin, Andrei V. Konstantinov:
Ensembles of Random SHAPs. Algorithms 15(11): 431 (2022) - [j69]Andrei V. Konstantinov, Lev V. Utkin:
Multi-attention multiple instance learning. Neural Comput. Appl. 34(16): 14029-14051 (2022) - [j68]Lev V. Utkin, Egor D. Satyukov, Andrei V. Konstantinov:
SurvNAM: The machine learning survival model explanation. Neural Networks 147: 81-102 (2022) - [j67]Lev V. Utkin, Andrei V. Konstantinov:
Attention-based random forest and contamination model. Neural Networks 154: 346-359 (2022) - [c18]Andrei V. Konstantinov, Lev V. Utkin, Stanislav Kirpichenko:
AGBoost: Attention-based Modification of Gradient Boosting Machine. FRUCT 2022: 96-101 - [c17]Andrei V. Konstantinov, Lev V. Utkin:
Multiple Instance Learning through Explanation by Using a Histopathology Example. FRUCT 2022: 102-108 - [i25]Lev V. Utkin, Andrei V. Konstantinov:
Attention-based Random Forest and Contamination Model. CoRR abs/2201.02880 (2022) - [i24]Lev V. Utkin, Andrei V. Konstantinov:
Attention and Self-Attention in Random Forests. CoRR abs/2207.04293 (2022) - [i23]Andrei V. Konstantinov, Lev V. Utkin, Stanislav Kirpichenko:
AGBoost: Attention-based Modification of Gradient Boosting Machine. CoRR abs/2207.05724 (2022) - [i22]Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin:
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. CoRR abs/2207.09139 (2022) - [i21]Lev V. Utkin, Andrey Y. Ageev, Andrei V. Konstantinov:
Improved Anomaly Detection by Using the Attention-Based Isolation Forest. CoRR abs/2210.02558 (2022) - [i20]Andrei V. Konstantinov, Lev V. Utkin:
LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models. CoRR abs/2210.05168 (2022) - [i19]Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov:
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. CoRR abs/2211.10793 (2022) - 2021
- [j66]Lev V. Utkin, Vladimir S. Zaborovsky, Maxim S. Kovalev, Andrei V. Konstantinov, Natalia A. Politaeva, Alexey Lukashin:
Uncertainty Interpretation of the Machine Learning Survival Model Predictions. IEEE Access 9: 120158-120175 (2021) - [j65]Maxim Kovalev, Lev V. Utkin, Frank P. A. Coolen, Andrei V. Konstantinov:
Counterfactual Explanation of Machine Learning Survival Models. Informatica 32(4): 817-847 (2021) - [j64]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable machine learning with an ensemble of gradient boosting machines. Knowl. Based Syst. 222: 106993 (2021) - [c16]Andrei V. Konstantinov, Lev V. Utkin, Vladimir Muliukha:
Gradient Boosting Machine with Partially Randomized Decision Trees. FRUCT 2021: 167-173 - [c15]Lev V. Utkin, Pavel D. Drobintsev, Maxim Kovalev, Andrei V. Konstantinov:
Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions. FRUCT 2021: 489-494 - [i18]Lev V. Utkin, Andrei V. Konstantinov:
Ensembles of Random SHAPs. CoRR abs/2103.03302 (2021) - [i17]Lev V. Utkin, Egor D. Satyukov, Andrei V. Konstantinov:
SurvNAM: The machine learning survival model explanation. CoRR abs/2104.08903 (2021) - [i16]Lev V. Utkin, Andrei V. Konstantinov, Kirill A. Vishniakov:
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data. CoRR abs/2106.09111 (2021) - [i15]Andrei V. Konstantinov, Lev V. Utkin:
Attention-like feature explanation for tabular data. CoRR abs/2108.04855 (2021) - [i14]Andrei V. Konstantinov, Lev V. Utkin:
Multi-Attention Multiple Instance Learning. CoRR abs/2112.06071 (2021) - 2020
- [j63]Anna A. Meldo, Lev V. Utkin, Maxim Kovalev, Ernest M. Kasimov:
The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif. Intell. Medicine 108: 101952 (2020) - [j62]Lev V. Utkin, Maxim S. Kovalev, Frank P. A. Coolen:
Imprecise weighted extensions of random forests for classification and regression. Appl. Soft Comput. 92: 106324 (2020) - [j61]Lev V. Utkin, Maxim Kovalev, Ernest M. Kasimov:
Explanation of Siamese Neural Networks for Weakly Supervised Learning. Comput. Informatics 39(6) (2020) - [j60]Lev V. Utkin:
An imprecise deep forest for classification. Expert Syst. Appl. 141 (2020) - [j59]Lev V. Utkin, Mikhail V. Kots, Viacheslav S. Chukanov, Andrei V. Konstantinov, Anna A. Meldo:
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. Int. J. Artif. Intell. Tools 29(5): 2050005:1-2050005:23 (2020) - [j58]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Anna A. Meldo:
A New Adaptive Weighted Deep Forest and Its Modifications. Int. J. Inf. Technol. Decis. Mak. 19(4): 963-986 (2020) - [j57]Lev V. Utkin, Kirill D. Zhuk:
Improvement of the Deep Forest Classifier by a Set of Neural Networks. Informatica (Slovenia) 44(1) (2020) - [j56]Maxim S. Kovalev, Lev V. Utkin, Ernest M. Kasimov:
SurvLIME: A method for explaining machine learning survival models. Knowl. Based Syst. 203: 106164 (2020) - [j55]Maxim S. Kovalev, Lev V. Utkin:
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. Neural Networks 132: 1-18 (2020) - [c14]Vladimir Muliukha, Alexey Lukashin, Lev V. Utkin, Mikhail Popov, Anna A. Meldo:
Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System. ESANN 2020: 251-256 - [i13]Maxim S. Kovalev, Lev V. Utkin, Ernest M. Kasimov:
SurvLIME: A method for explaining machine learning survival models. CoRR abs/2003.08371 (2020) - [i12]Maxim S. Kovalev, Lev V. Utkin:
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds. CoRR abs/2005.02249 (2020) - [i11]Lev V. Utkin, Maxim S. Kovalev, Ernest M. Kasimov:
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models. CoRR abs/2005.02387 (2020) - [i10]Andrei V. Konstantinov, Lev V. Utkin:
Gradient boosting machine with partially randomized decision trees. CoRR abs/2006.11014 (2020) - [i9]Maxim S. Kovalev, Lev V. Utkin:
Counterfactual explanation of machine learning survival models. CoRR abs/2006.16793 (2020) - [i8]Andrei V. Konstantinov, Lev V. Utkin:
A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines. CoRR abs/2010.06026 (2020) - [i7]Andrei V. Konstantinov, Lev V. Utkin:
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines. CoRR abs/2010.07388 (2020)
2010 – 2019
- 2019
- [j54]Lev V. Utkin, Mikhail A. Ryabinin:
Discriminative Metric Learning with Deep Forest. Int. J. Artif. Intell. Tools 28(2): 1950007:1-1950007:19 (2019) - [j53]Lev V. Utkin:
An imprecise extension of SVM-based machine learning models. Neurocomputing 331: 18-32 (2019) - [j52]Lev V. Utkin, Maxim Kovalev, Anna A. Meldo:
A deep forest classifier with weights of class probability distribution subsets. Knowl. Based Syst. 173: 15-27 (2019) - [j51]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Mikhail A. Ryabinin, Anna A. Meldo:
A weighted random survival forest. Knowl. Based Syst. 177: 136-144 (2019) - [c13]Anna A. Meldo, Lev V. Utkin, Alexey Lukashin, Vladimir Muliukha, Vladimir S. Zaborovsky:
Database Acquisition for the Lung Cancer Computer Aided Diagnostic Systems. FRUCT 2019: 220-227 - [c12]Lev V. Utkin, Anna A. Meldo, Maxim Kovalev, Ernest M. Kasimov:
An Ensemble of Triplet Neural Networks for Differential Diagnostics of Lung Cancer. FRUCT 2019: 346-352 - [c11]Lev V. Utkin, Andrei V. Konstantinov, Anna A. Meldo, Mikhail A. Ryabinin, Viacheslav S. Chukanov:
A Deep Forest Improvement by Using Weighted Schemes. FRUCT 2019: 451-456 - [c10]Lev V. Utkin, Maxim Kovalev, Anna A. Meldo, Frank P. A. Coolen:
Imprecise Extensions of Random Forests and Random Survival Forests. ISIPTA 2019: 404-413 - [i6]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Mikhail A. Ryabinin, Anna A. Meldo:
A weighted random survival forest. CoRR abs/1901.00213 (2019) - [i5]Lev V. Utkin, Andrei V. Konstantinov, Viacheslav S. Chukanov, Mikhail V. Kots, Anna A. Meldo:
An Adaptive Weighted Deep Forest Classifier. CoRR abs/1901.01334 (2019) - [i4]Lev V. Utkin, Mikhail V. Kots, Viacheslav S. Chukanov:
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. CoRR abs/1909.03894 (2019) - [i3]Lev V. Utkin, Maxim S. Kovalev, Ernest M. Kasimov:
An explanation method for Siamese neural networks. CoRR abs/1911.07702 (2019) - 2018
- [j50]Lev V. Utkin, Yulia A. Zhuk:
A modification of the Lasso method by using the Bahadur representation for the genome-wide association study. Informatica (Slovenia) 42(2) (2018) - [j49]Lev V. Utkin, Mikhail A. Ryabinin:
A Siamese Deep Forest. Knowl. Based Syst. 139: 13-22 (2018) - [j48]Lev V. Utkin, Frank P. A. Coolen:
A robust weighted SVR-based software reliability growth model. Reliab. Eng. Syst. Saf. 176: 93-101 (2018) - 2017
- [j47]Sergey V. Gurov, Lev V. Utkin:
Reliability of repairable reserved systems with failure aftereffect. Autom. Remote. Control. 78(1): 113-124 (2017) - [j46]Lev V. Utkin, Vladimir S. Zaborovsky, Sergey G. Popov:
Siamese neural network for intelligent information security control in multi-robot systems. Autom. Control. Comput. Sci. 51(8): 881-887 (2017) - [j45]Lev V. Utkin, Yulia A. Zhuk:
Interval SVM-Based Classification Algorithm Using the Uncertainty Trick. Int. J. Artif. Intell. Tools 26(4): 1750014:1-1750014:32 (2017) - [j44]Lev V. Utkin, Yulia A. Zhuk:
An one-class classification support vector machine model by interval-valued training data. Knowl. Based Syst. 120: 43-56 (2017) - [i2]Lev V. Utkin, Mikhail A. Ryabinin:
A Siamese Deep Forest. CoRR abs/1704.08715 (2017) - [i1]Lev V. Utkin, Mikhail A. Ryabinin:
Discriminative Metric Learning with Deep Forest. CoRR abs/1705.09620 (2017) - 2016
- [j43]Lev V. Utkin, V. S. Zaborovskii, Sergey G. Popov:
Detection of anomalous behavior in a robot system based on deep learning elements. Autom. Control. Comput. Sci. 50(8): 726-733 (2016) - [j42]Lev V. Utkin, Anatoly I. Chekh, Yulia A. Zhuk:
Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels. Neural Networks 80: 53-66 (2016) - 2015
- [j41]Lev V. Utkin, Yulia A. Zhuk:
Robust Classifiers Using Imprecise Probability Models and Importance of Classes. Int. J. Artif. Intell. Tools 24(1): 1550008:1-1550008:28 (2015) - [j40]Lev V. Utkin:
The imprecise Dirichlet model as a basis for a new boosting classification algorithm. Neurocomputing 151: 1374-1383 (2015) - [j39]Lev V. Utkin, Andrea Wiencierz:
Improving over-fitting in ensemble regression by imprecise probabilities. Inf. Sci. 317: 315-328 (2015) - [j38]Lev V. Utkin, Anatoly I. Chekh:
A new robust model of one-class classification by interval-valued training data using the triangular kernel. Neural Networks 69: 99-110 (2015) - [j37]Lev V. Utkin, Frank P. A. Coolen, Sergey V. Gurov:
Imprecise inference for warranty contract analysis. Reliab. Eng. Syst. Saf. 138: 31-39 (2015) - 2014
- [j36]Lev V. Utkin, Yulia A. Zhuk:
Imprecise prior knowledge incorporating into one-class classification. Knowl. Inf. Syst. 41(1): 53-76 (2014) - [j35]Lev V. Utkin, Yulia A. Zhuk:
Robust boosting classification models with local sets of probability distributions. Knowl. Based Syst. 61: 59-75 (2014) - [j34]Lev V. Utkin:
A framework for imprecise robust one-class classification models. Int. J. Mach. Learn. Cybern. 5(3): 379-393 (2014) - [c9]Lev V. Utkin, Yulia A. Zhuk, Anatoly I. Chekh:
A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy. MLDM 2014: 107-118 - 2013
- [j33]Lev V. Utkin, Yulia A. Zhuk:
Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. Adv. Artif. Intell. 2013: 176890:1-176890:12 (2013) - [j32]Lev V. Utkin, Yulia A. Zhuk:
Robust novelty detection in the framework of a contamination neighbourhood. Int. J. Intell. Inf. Database Syst. 7(3): 205-224 (2013) - [j31]Lev V. Utkin, Yulia A. Zhuk:
Fuzzy decision making using the imprecise Dirichlet model. Int. J. Math. Oper. Res. 5(1): 74-90 (2013) - [j30]Lev V. Utkin:
An Imprecise Boosting-like Approach to Classification. Int. J. Pattern Recognit. Artif. Intell. 27(8) (2013) - 2012
- [j29]Lev V. Utkin:
Fuzzy One-Class Classification Model Using Contamination Neighborhoods. Adv. Fuzzy Syst. 2012: 984325:1-984325:10 (2012) - [j28]Lev V. Utkin, Yulia A. Zhuk:
Combining of judgments in imprecise voting multi-criteria decision problems. Int. J. Appl. Decis. Sci. 5(3): 199-214 (2012) - [j27]Lev V. Utkin, Yulia A. Zhuk:
A machine learning algorithm for classification under extremely scarce information. Int. J. Data Anal. Tech. Strateg. 4(2): 115-133 (2012) - [j26]Lev V. Utkin, Natalia V. Simanova:
The DS/AHP Method under Partial Information about Criteria and Alternatives by Several Levels of Criteria. Int. J. Inf. Technol. Decis. Mak. 11(2): 307-326 (2012) - [c8]Van Hieu Nguyen, Lev V. Utkin, Dang Duy Thang:
A Pessimistic Approach for Solving a Multi-criteria Decision Making. KSE 2012: 121-127 - 2011
- [r1]Frank P. A. Coolen, Lev V. Utkin:
Imprecise Reliability. International Encyclopedia of Statistical Science 2011: 649-650 - 2010
- [j25]Lev V. Utkin:
Regression analysis using the imprecise Bayesian normal model. Int. J. Data Anal. Tech. Strateg. 2(4): 356-372 (2010)
2000 – 2009
- 2009
- [j24]Lev V. Utkin:
A new ranking procedure by incomplete pairwise comparisons using preference subsets. Intell. Data Anal. 13(2): 229-241 (2009) - [j23]Lev V. Utkin, Sébastien Destercke:
Computing expectations with continuous p-boxes: Univariate case. Int. J. Approx. Reason. 50(5): 778-798 (2009) - 2007
- [j22]Lev V. Utkin:
Second-order uncertainty calculations by using the imprecise Dirichlet model. Intell. Data Anal. 11(3): 225-244 (2007) - [j21]Lev V. Utkin, Thomas Augustin:
Decision making under incomplete data using the imprecise Dirichlet model. Int. J. Approx. Reason. 44(3): 322-338 (2007) - [j20]Lev V. Utkin:
Risk Analysis under Partial Prior Information and Nonmonotone Utility Functions. Int. J. Inf. Technol. Decis. Mak. 6(4): 625-647 (2007) - [p1]Lev V. Utkin, Frank P. A. Coolen:
Imprecise reliability: An introductory overview. Intelligence in Reliability Engineering 2007: 261-306 - 2006
- [j19]Lev V. Utkin:
Ranking procedures by pairwise comparison using random sets and the imprecise Dirichlet model. Appl. Math. Comput. 183(1): 394-408 (2006) - [j18]Lev V. Utkin:
A method for processing the unreliable expert judgments about parameters of probability distributions. Eur. J. Oper. Res. 175(1): 385-398 (2006) - [j17]Lev V. Utkin:
Cautious Analysis of Project Risks by Interval-Valued Initial Data. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 14(6): 663-685 (2006) - 2005
- [j16]Lev V. Utkin:
Comments on the paper "A behavioural model for vague probability assessments" by Gert de Cooman. Fuzzy Sets Syst. 154(3): 367-369 (2005) - [j15]Lev V. Utkin:
Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model. Fuzzy Sets Syst. 154(3): 413-431 (2005) - [j14]Igor Kozine, Lev V. Utkin:
Constructing imprecise probability distributions. Int. J. Gen. Syst. 34(4): 401-408 (2005) - [j13]Lev V. Utkin:
Imprecise second-order model for a system of independent random variables. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 13(2): 177-193 (2005) - [j12]Lev V. Utkin, Igor Kozine:
Computing System Reliability Given Interval-Valued Characteristics of the Components. Reliab. Comput. 11(1): 19-34 (2005) - [c7]Lev V. Utkin, Thomas Augustin:
Powerful algorithms for decision making under partial prior information and general ambiguity attitudes. ISIPTA 2005: 349-358 - [c6]Lev V. Utkin, Thomas Augustin:
Decision making under incomplete data using the imprecise Dirichlet model. ISIPTA 2005: 359-368 - 2004
- [j11]Lev V. Utkin:
Reliability models of m-out-of-n systems under incomplete information. Comput. Oper. Res. 31(10): 1681-1702 (2004) - [j10]Lev V. Utkin:
Interval reliability of typical systems with partially known probabilities. Eur. J. Oper. Res. 153(3): 790-802 (2004) - [j9]Igor Kozine, Lev V. Utkin:
An approach to combining unreliable pieces of evidence and their propagation in a system response analysis. Reliab. Eng. Syst. Saf. 85(1-3): 103-112 (2004) - [j8]Lev V. Utkin:
A new efficient algorithm for computing the imprecise reliability of monotone systems. Reliab. Eng. Syst. Saf. 86(3): 179-190 (2004) - 2003
- [j7]Lev V. Utkin:
Imprecise Second-Order Hierarchical Uncertainty Model . Int. J. Uncertain. Fuzziness Knowl. Based Syst. 11(3): 301-318 (2003) - [j6]Lev V. Utkin:
A second-order uncertainty model for calculation of the interval system reliability. Reliab. Eng. Syst. Saf. 79(3): 341-351 (2003) - [c5]Lev V. Utkin:
A Second-Order Uncertainty Model of Independent Random Variables: An Example of the Stress-Strength Reliability. ISIPTA 2003: 530-544 - [c4]Lev V. Utkin:
Decision Making with Imprecise Second-Order Probabilities. ISIPTA 2003: 545-559 - 2002
- [j5]Lev V. Utkin, Igor Kozine:
Stress-strength reliability models under incomplete information. Int. J. Gen. Syst. 31(6): 549-568 (2002) - [j4]Igor Kozine, Lev V. Utkin:
Interval-Valued Finite Markov Chains. Reliab. Comput. 8(2): 97-113 (2002) - 2001
- [c3]Igor Kozine, Lev V. Utkin:
Different faces of the natural extension. ISIPTA 2001: 316-323 - [c2]Igor Kozine, Lev V. Utkin:
Computing the reliability of complex systems. ISIPTA 2001: 324-331
1990 – 1999
- 1999
- [j3]Lev V. Utkin, Sergey V. Gurov:
Imprecise Reliability of General Structures. Knowl. Inf. Syst. 1(4): 459-480 (1999) - [c1]Lev V. Utkin, Sergey V. Gurov:
Imprecise Reliability Models for the General Lifetime Distribution Classes. ISIPTA 1999: 333-342 - 1998
- [j2]Lev V. Utkin, Sergey V. Gurov:
Steady-state reliability of repairable systems by combined probability and possibility assumptions. Fuzzy Sets Syst. 97(2): 193-202 (1998) - 1996
- [j1]Lev V. Utkin, Sergey V. Gurov:
A general formal approach for fuzzy reliability analysis in the possibility context. Fuzzy Sets Syst. 83(2): 203-213 (1996)
Coauthor Index
Stanislav Kirpichenko
aka: Stanislav R. Kirpichenko
aka: Stanislav R. Kirpichenko
[j78] [i36] [i35] [i34] [i32] [j77] [j76] [j75] [j74] [j73] [j72] [j71] [c19] [i31] [i30] [i29] [i28] [i27] [i26] [j70] [j69] [j68] [j67] [c18] [c17] [i25] [i24] [i23] [i22] [i21] [i20] [i19] [j66] [j65] [j64] [c16] [c15] [i18] [i17] [i16] [i15] [i14] [j59] [j58] [i10] [i8] [i7] [j51] [c11] [i6] [i5]
Maxim Kovalev
aka: Maxim S. Kovalev
aka: Maxim S. Kovalev
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last updated on 2024-11-05 22:03 CET by the dblp team
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