Krzysztof Gajowniczek

发表

Independent Component Analysis for Ensemble Predictors with Small Number of Models

R. Szupiluk, T. Zabkowski, Krzysztof Gajowniczek, 2015 .

HOFS: Higher order mutual information approximation for feature selection in R

Krzysztof Gajowniczek, Jialin Wu, Soumyajit Gupta, 2022, SoftwareX.

Insolvency modeling with generalized entropy cost function in neural networks

Krzysztof Gajowniczek, Tomasz Ząbkowski, Arkadiusz Orłowski, 2019, Physica A: Statistical Mechanics and its Applications.

Grade Analysis for households segmentation based on energy usage patterns

Krzysztof Gajowniczek, Tomasz Zabkowski, T. Zabkowski, 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Krzysztof Gajowniczek, Tomasz Ząbkowski, Iga Grzegorczyk, 2020, Electronics.

Weighted Random Forests to Improve Arrhythmia Classification

Krzysztof Gajowniczek, Tomasz Ząbkowski, Chandrajit Bajaj, 2020, Electronics.

ImbTreeEntropy and ImbTreeAUC: Novel R Packages for Decision Tree Learning on the Imbalanced Datasets

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2021, Electronics.

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

Krzysztof Gajowniczek, Tomasz Zabkowski, Iga Grzegorczyk, 2019, Sensors.

Seasonal Peak Demand Classification with Machine Learning Techniques

Krzysztof Gajowniczek, Tomasz Ząbkowski, Rafik Nafkha, 2018, 2018 International Conference on Applied Mathematics & Computer Science (ICAMCS).

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Krzysztof Gajowniczek, Arkadiusz Orlowski, Tomasz Zabkowski, 2018, Entropy.

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2017 .

Electricity peak demand classification with artificial neural networks

Krzysztof Gajowniczek, Tomasz Zabkowski, Rafik Nafkha, 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

Estimating the ROC Curve and Its Significance for Classification Models' Assessment

Ryszard Szupiluk, Krzysztof Gajowniczek, Tomasz Ząbkowski, 2014 .

Comparison of decision trees with Rényi and Tsallis entropy applied for imbalanced churn dataset

Krzysztof Gajowniczek, Arkadiusz Orlowski, Tomasz Zabkowski, 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

Forecasting of Individual Electricity Usage Using Smart Meter Data

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2013 .

Electricity forecasting on the individual household level enhanced based on activity patterns

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2017, PloS one.

Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case

Krzysztof Gajowniczek, Michał Gostkowski, Krzysztof Gajowniczek, 2020, Entropy.

Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

Krzysztof Gajowniczek, Tomasz Zabkowski, T. Zabkowski, 2018, Complex..

Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2015 .

ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2021, SoftwareX.

Short Term Electricity Forecasting Using Individual Smart Meter Data

Krzysztof Gajowniczek, Tomasz Zabkowski, T. Zabkowski, 2014, KES.

Interactive Decision Tree Learning and Decision Rule Extraction Based on the ImbTreeEntropy and ImbTreeAUC Packages

Krzysztof Gajowniczek, Tomasz Ząbkowski, T. Zabkowski, 2021, Processes.

Simulation Study on the Electricity Data Streams Time Series Clustering

Chu Kiong Loo, Krzysztof Gajowniczek, Tomasz Ząbkowski, 2020, Energies.

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Krzysztof Gajowniczek, Tomasz Ząbkowski, Marcin Bator, 2020, Entropy.

Generalized Entropy Cost Function in Neural Networks

Krzysztof Gajowniczek, Arkadiusz Orlowski, Tomasz Zabkowski, 2017, ICANN.

Extended AMUSE Algorithm and Novel Randomness Approach for BSS Model Aggregation with Methodology Remarks

Ryszard Szupiluk, Krzysztof Gajowniczek, Tomasz Zabkowski, 2015, ICAISC.

Q-Entropy Approach to Selecting High Income Households

Krzysztof Gajowniczek, Tomasz Ząbkowski, Arkadiusz Orłowski, 2015 .

Short term electricity forecasting based on user behavior from individual smart meter data

Krzysztof Gajowniczek, Tomasz Zabkowski, T. Zabkowski, 2015, J. Intell. Fuzzy Syst..

Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques

Krzysztof Gajowniczek, Tomasz Ząbkowski, Rafik Nafkha, 2018 .

Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland

Grzegorz Mentel, J. Jarnicka, T. Zabkowski, 2023, Energies.

Blind Source Separation for Improved Load Forecasting on Individual Household Level

Ryszard Szupiluk, Krzysztof Gajowniczek, Tomasz Zabkowski, 2015, CORES.

Foresttaxator: A Tool for Detection and Approximation of Cross-Sectional Area of Tree in Cloud of 3d Points

Krzysztof Gajowniczek, A. Zembrzuski, Maciej Małaszek, 2022, SSRN Electronic Journal.

Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms

Mariya A. Sodenkamp, Krzysztof Gajowniczek, Tomasz Ząbkowski, 2018, Applied Sciences.