I. Kononenko

发表

Information-based evaluation criterion for classifier's performance

I. Bratko, I. Kononenko, 2004, Machine Learning.

Experiments in automatic learning of medical diagnostic rules

Ivan Bratko, Igor Kononenko, E. Roskar, 1984 .

ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users

Ivan Bratko, Igor Kononenko, Bojan Cestnik, 1987, EWSL.

Semi-Naive Bayesian Classifier

Igor Kononenko, I. Kononenko, 1991, EWSL.

Explaining Classifications For Individual Instances

Marko Robnik-Sikonja, Igor Kononenko, I. Kononenko, 2008, IEEE Transactions on Knowledge and Data Engineering.

Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF

Marko Robnik-Sikonja, Igor Kononenko, Edvard Simec, 2004, Applied Intelligence.

Estimating Attributes: Analysis and Extensions of RELIEF

Igor Kononenko, I. Kononenko, 1994, ECML.

An Efficient Explanation of Individual Classifications using Game Theory

Erik Strumbelj, Igor Kononenko, I. Kononenko, 2010, J. Mach. Learn. Res..

Machine learning improves the accuracy of coronary artery disease diagnostic methods

I. Kononenko, C. Groselj, M. Kukar, 1997, Computers in Cardiology 1997.

Machine Learning and Data Mining: Introduction to Principles and Algorithms

Igor Kononenko, Matjaz Kukar, I. Kononenko, 2007 .

Prognosing the Survival Time of Patients with Anaplastic Thyroid Carcinoma using Machine Learning

I. Kononenko, M. Robnik-Sikonja, M. Kukar, 1997 .

Bias and pathology in minimax search

Ivan Bratko, Aleksander Sadikov, Igor Kononenko, 2005, Theor. Comput. Sci..

Towards Reliable Reliability Estimates for Individual Regression Predictions Technical Report T-01 / 08

Igor Kononenko, Zoran Bosnić, I. Kononenko, 2008 .

Method of Selecting the Maturity Level of an Organization When Managing a Project Portfolio

I. Kononenko, Maximilien Kpodjedo, 2020, 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT).

Attribute Dependencies, Understandability and Split Selection in Tree Based Models

Marko Robnik-Sikonja, Igor Kononenko, I. Kononenko, 1999, ICML.

Application of the Project Management Methodology Formation’s Method

Igor Kononenko, Svitlana Lutsenko, I. Kononenko, 2019 .

The Use of Prediction Reliability Estimates on Imbalanced Datasets: A Case Study of Wall Shear Stress in the Human Carotid Artery Bifurcation

I. Kononenko, Z. Bosnić, Domen Kosir, 2013 .

Induction of decision trees using RELIEFFIgor Kononenko

I. Kononenko, E. Simec, 1995 .

Prognosing the femoral neck fracture recovery with machine learning

I. Kononenko, M. Kukar, T. Silvester, 1998 .

Diagnosing the Ischaemic Heart Disease with Machine Learning

I. Kononenko, C. Groselj, M. Kukar, 1998 .

Mathematical model of software development project team composition optimization with fuzzy initial data

I. Kononenko, Hlib Sushko, 2021, RADIOELECTRONIC AND COMPUTER SYSTEMS.

EXPLANATION AND RELIABILITY OF INDIVIDUAL PREDICTIONS : RECENT RESEARCH BY LKM

I. Kononenko, M. Robnik-Sikonja, E. Štrumbelj, 2012 .

Automatic construction of the knowledge base for a steel classification expert system

Ivan Bratko, Matjaz Gams, Nada Lavrač, 1987 .

Classification of grapevine cultivars using Kirlian camera and machine learning.

I. Kononenko, I. Kononenko, D. Skočaj, 2000 .

Estimation of Prediction Reliability in Regression Based on a Transductive Approach

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2005, IICAI.

Successive Naive Bayesian Classifier

Igor Kononenko, I. Kononenko, 1993, Informatica.

Method for solving the multi-criteria non-Markov problem of project portfolio optimization

I. Kononenko, A. Korchakova, 2022, RADIOELECTRONIC AND COMPUTER SYSTEMS.

Chapter 5 – Learning as Search

Igor Kononenko, I. Kononenko, 2007 .

Machine Learning and Data Mining: Introduction to Principles and Algorithms

Igor Kononenko, Matjaž Kukar, I. Kononenko, 2007 .

Chapter 8 – *Constructive Induction

Igor Kononenko, I. Kononenko, 2007 .

Chapter 11 – Artificial Neural Networks

Igor Kononenko, I. Kononenko, 2007 .

Closed world specialisation inside the induction process

Igor Kononenko, Miha Drole, I. Kononenko, 2016, Intelligent Data Analysis.

Non-Myopic Feature Quality Evaluation with (R)ReliefF

Igor Kononenko, Igor Kononenko, Marko Robnik ¡Sikonja, 2007 .

Automatic attribute construction for basketball modelling

Igor Kononenko, Erik Štrumbelj, Petar Vračar, 2019, Knowledge and Information Systems.

Analysing and improving the diagnosis of ischaemic heart disease with machine learning

Igor Kononenko, Matjaz Kukar, Ciril Groselj, 1999, Artif. Intell. Medicine.

Image Segmentation and Parameterization for Automatic Diagnostics of Whole-Body Scintigrams: Basic Concepts

Igor Kononenko, Luka Šajn, I. Kononenko, 2009 .

Empirical Analysis of Reliability Estimates for Individual Regression Predictions

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2008, DaWaK.

Early Machine Learning Research in Ljubljana

Igor Kononenko, I. Kononenko, 2018, Informatica.

Machine learning for medical diagnosis: history, state of the art and perspective

Igor Kononenko, I. Kononenko, 2001, Artif. Intell. Medicine.

Intelligent Data Analysis for Medical Diagnosis: Using Machine Learning and Temporal Abstraction

Elpida T. Keravnou, Nada Lavrac, Blaz Zupan, 1998, AI Commun..

The Minimum Description Length Based Decision Tree Pruning

Igor Kononenko, I. Kononenko, 1998, PRICAI.

Experiments with Machine Learning in the Prediction of Coronary Artery Disease Progression

Igor Kononenko, Andrej Dobnikar, Matjaž Kukar, 1997 .

Machine learning in prognosis of the femoral neck fracture recovery

Igor Kononenko, Matjaz Kukar, T. Silvester, 1996, Artif. Intell. Medicine.

A counter example to the stronger version of the binarytree hypothesisIgor

Igor Kononenko, I. Kononenko, 1995 .

Inductive and Bayesian learning in medical diagnosis

Igor Kononenko, I. Kononenko, 1993, Appl. Artif. Intell..

An experiment in machine learning of redundant knowledge

I. Kononenko, I. Kononenko, 1991, [1991 Proceedings] 6th Mediterranean Electrotechnical Conference.

Explaining prediction models and individual predictions with feature contributions

Erik Strumbelj, Igor Kononenko, I. Kononenko, 2014, Knowledge and Information Systems.

Machine learning from coronas using parametrization of images

Aleksander Sadikov, Igor Kononenko, Matjaž Bevk, 2003 .

Analyzing Coronas of Fruits and Leaves

Aleksander Sadikov, Igor Kononenko, Franco Weibel, 2004 .

Comprehensible evaluation of prognostic factors and prediction of wound healing

Marko Robnik-Sikonja, David Cukjati, Igor Kononenko, 2003, Artif. Intell. Medicine.

A statistical approach to texture description of medical images: a preliminary study

Matjaz Bevk, Igor Kononenko, I. Kononenko, 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

Pruning Regression Trees with MDL

Marko Robnik-Sikonja, Igor Kononenko, I. Kononenko, 1998, ECAI.

An adaptation of Relief for attribute estimation in regression

Marko Robnik-Sikonja, Igor Kononenko, I. Kononenko, 1997, ICML.

Attribute-Based Learning

Ivan Bratko, Igor Kononenko, Bojan Cestnik, 1996, AI Commun..

Theoretical and Empirical Analysis of ReliefF and RReliefF

Marko Robnik-Sikonja, Igor Kononenko, I. Kononenko, 2003, Machine Learning.

Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease

Igor Kononenko, Matjaz Kukar, Ciril Groselj, 2011, Artif. Intell. Medicine.

INDUCTION OF DECISION TREES USING RELIEFF

I. Kononenko, E. Simec, I. Kononenko, 1995 .

Medical Analysis of Automatically Induced Diagnostic Rules

Ivan Bratko, Igor Kononenko, V. Pirnat, 1989, AIME.

Business Game for PMBoK Standard Training of Pro-ject Managers

Natalia Kryvinska, Igor Kononenko, Oleksandra Stepanova, 2020, ITPM.

Computerized segmentation and diagnostics of whole-body bone scintigrams

Igor Kononenko, Luka Sajn, Metka Milcinski, 2007, Comput. Medical Imaging Graph..

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics

Igor Kononenko, Matjaz Kukar, Luka Sajn, 2005, Comput. Methods Programs Biomed..

Efficiently explaining the predictions of a probabilistic radial basis function classification network

Erik Strumbelj, Marko Robnik-Sikonja, Igor Kononenko, 2013, Intell. Data Anal..

Quality of classification explanations with PRBF

Erik Strumbelj, Marko Robnik-Sikonja, Igor Kononenko, 2012, Neurocomputing.

Towards a Model Independent Method for Explaining Classification for Individual Instances

Erik Strumbelj, Igor Kononenko, I. Kononenko, 2008, DaWaK.

An Efficient Method for Explaining the Decisions of the Probabilistic RBF Classification Network

Aristidis Likas, Igor Kononenko, Constantinos Constantinopoulos, 2007 .

Feedforward Bayesian neural network and continuous attributes

I. Kononenko, I. Kononenko, 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

On Biases in Estimating Multi-Valued Attributes

Igor Kononenko, I. Kononenko, 1995, IJCAI.

ReliefF for estimation and discretization of attributes in classification, regression, and ILP probl

Igor Kononenko, M. Robnik Sikonja, Uros Pompe, 1996 .

Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation Models

Nenad Filipovic, Igor Kononenko, Milos D. Radovic, 2010, IEEE Transactions on Information Technology in Biomedicine.

Cost-Sensitive Learning with Neural Networks

Igor Kononenko, Matjaz Kukar, I. Kononenko, 1998, ECAI.

Explaining instance classifications with interactions of subsets of feature values

Erik Strumbelj, Marko Robnik-Sikonja, Igor Kononenko, 2009, Data Knowl. Eng..

Extended Symbolic Mining of Textures with Association Rules

Matjaz Bevk, Igor Kononenko, I. Kononenko, 2009, Informatica.

Non-Myopic Attribute Estimation in Regression

Igor Kononenko, M. Robnik Sikonja, I. Kononenko, 1996 .

Learning as Optimization: Stochastic Generation of Multiple Knowledge

Igor Kononenko, Matevz Kovacic, I. Kononenko, 1992, ML.

Combining Decisions of Multiple Rules

Igor Kononenko, I. Kononenko, 1992, AIMSA.

An application of machine learning in the diagnosis of ischaemic heart disease

Igor Kononenko, Matjaz Kukar, Ciril Groselj, 1997, Proceedings of Computer Based Medical Systems.

Information-Based Evaluation Criterion for Classifier's Performance

Ivan Bratko, Igor Kononenko, I. Bratko, 1991, Machine Learning.

Pairwise saturations in inductive logic programming

Igor Kononenko, Miha Drole, I. Kononenko, 2017, Artificial Intelligence Review.

Explanation and Reliability of Individual Predictions

Erik Strumbelj, Marko Robnik-Sikonja, Darko Pevec, 2013, Informatica.

Correcting Streaming Predictions of an Electricity Load Forecast System Using a Prediction Reliability Estimate

João Gama, Igor Kononenko, Pedro Pereira Rodrigues, 2011, ICMMI.

Weighted hierarchical archetypal analysis for multi-document summarization

Igor Kononenko, Ercan Canhasi, I. Kononenko, 2016, Comput. Speech Lang..

Automatic Extractive Multi-document Summarization Based on Archetypal Analysis

Igor Kononenko, Ercan Canhasi, I. Kononenko, 2016 .

Efficiently Explaining Decisions of Probabilistic RBF Classification Networks

Erik Strumbelj, Aristidis Likas, Marko Robnik-Sikonja, 2011, ICANNGA.

Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms

Erik Strumbelj, Igor Kononenko, I. Kononenko, 2018, Human and Machine Learning.

Enhancing data stream predictions with reliability estimators and explanation

João Gama, Igor Kononenko, Pedro Pereira Rodrigues, 2014, Eng. Appl. Artif. Intell..

Automatic selection of reliability estimates for individual regression predictions

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2010, The Knowledge Engineering Review.

An overview of advances in reliability estimation of individual predictions in machine learning

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2009, Intell. Data Anal..

Comparison of approaches for estimating reliability of individual regression predictions

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2008, Data Knowl. Eng..

Estimation of individual prediction reliability using the local sensitivity analysis

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2008, Applied Intelligence.

Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs

Igor Kononenko, Zoran Bosnic, I. Kononenko, 2010, Comput. Informatics.

An Application of Ilp in a Musical Database: Learning to Compose the Two-voice Counterpoint

Igor Kononenko, I. Kononenko, 1996 .

Linear Space Induction in First Order Logic with Relieff

I. Kononenko, U. Pompe, I. Kononenko, 1995 .

Web user profiles with time-decay and prototyping

Igor Kononenko, Zoran Bosnic, Domen Kosir, 2014, Applied Intelligence.

Bayesian neural networks

Igor Kononenko, I. Kononenko, Igor Kononenko, 1989, Biological Cybernetics.

Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries

Nada Lavrač, Igor Kononenko, Igor Zelič, 1997, Journal of Medical Systems.

CLASSIFICATION OF DIFFERENT TYPES OF CORONAS USING PARAMETRIZATION OF IMAGES AND MACHINE LEARNING

Matjaz Bevk, Igor Kononenko, Luka Šajn, 2004 .

Texture Analysis using Association Rules

Igor Kononenko, Matjaž Bevk, I. Kononenko, 2004 .

Attribute selection for modelling

Igor Kononenko, Se June Hong, I. Kononenko, 1997, Future Gener. Comput. Syst..

Attribute Selection for Modeling

I. Kononenko, I. Kononenko, 1997 .

Frequent subgraph mining in oceanographic multi-level directed graphs

Igor Kononenko, Boris Petelin, Matjaž Kukar, 2019, Int. J. Geogr. Inf. Sci..

Evaluation of Prognostic Factors and Prediction of Chronic Wound Healing Rate by Machine Learning Tools

Marko Robnik-Sikonja, David Cukjati, Igor Kononenko, 2001, AIME.

Prediction intervals in supervised learning for model evaluation and discrimination

I. Kononenko, Darko Pevec, 2015, Applied Intelligence.

INFORMACIJSKA DRUBA IS 2006 INFORMATION SOCIETY IS 2006

I. Kononenko, M. Gams, J. Musek, 2006 .

Multi-level association rules and directed graphs for spatial data analysis

Igor Kononenko, Matjaz Kukar, Boris Petelin, 2013, Expert Syst. Appl..

Explanation and reliability of prediction models: the case of breast cancer recurrence

Erik Strumbelj, Igor Kononenko, Zoran Bosnic, 2010, Knowledge and Information Systems.

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics

I. Kononenko, M. Kukar, L. Sajn, 2005, Comput. Methods Programs Biomed..

Automatic Segmentation of Whole-Body Bone Scintigrams as a Preprocessing Step for Computer Assisted Diagnostics

Igor Kononenko, Matjaz Kukar, Luka Sajn, 2005, AIME.

Multiresolution Image Parametrization for Improving Texture Classification

Igor Kononenko, Luka Sajn, I. Kononenko, 2008, EURASIP J. Adv. Signal Process..

Evaluating Reliability of Single Classifications of Neural Networks

Erik Strumbelj, Igor Kononenko, Darko Pevec, 2011, ICANNGA.

The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines

Igor Kononenko, Janez Plavec, Gregor Serša, 2018, Chembiochem : a European journal of chemical biology.

Comparison of approaches for estimating reliability of individual regression predictions

I. Kononenko, Z. Bosnić, 2008, Data Knowl. Eng..

Evaluation of prediction reliability in regression using the transduction principle

Z. Bosnic, I. Kononenko, M. Robnik-Sikonja, 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

A Fast Algorithm for Mining Utility-Frequent Itemsets

Nada Lavrač, Igor Kononenko, Vid Podpečan, 2007 .

Efficient Derivation of the Optimal Assembly Sequence from Product Description

Igor Kononenko, D. Noe, Samo Zorc, 1998, Cybern. Syst..

Towards symbolic mining of images with association rules: Preliminary results on textures

Matjaz Bevk, Igor Kononenko, I. Kononenko, 2006, Intell. Data Anal..