Pawel Teisseyre

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 29

Top Topics

Logistic Regression

Feature Selection

Mutual Information

Top Venues

Pattern Recognit.

Publications

Wangduk Seo, Sung-Hyun Cho, Pawel Teisseyre, Jaesung Lee
A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features. IEEE Access 12 (2024)
Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. CoRR (2024)
Pawel Teisseyre, Jaesung Lee
Multilabel all-relevant feature selection using lower bounds of conditional mutual information. Expert Syst. Appl. 216 (2023)
Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee
Cost-constrained Group Feature Selection Using Information Theory. MDAI (2023)
Tae-Won Lee, Pawel Teisseyre, Jaesung Lee
Effective Exploitation of Macroeconomic Indicators for Stock Direction Classification Using the Multimodal Fusion Transformer. IEEE Access 11 (2023)
Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee
Cost-constrained feature selection in multilabel classification using an information-theoretic approach. Pattern Recognit. 141 (2023)
Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre
Double Logistic Regression Approach to Biased Positive-Unlabeled Data. ECAI (2023)
Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk
Joint empirical risk minimization for instance-dependent positive-unlabeled data. CoRR (2023)
Tomasz Klonecki, Pawel Teisseyre
Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods. Appl. Intell. 53 (24) (2023)
Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre
Joint estimation of posterior probability and propensity score function for positive and unlabelled data. CoRR (2022)
Pawel Teisseyre
Joint Feature Selection and Classification for Positive Unlabelled Multi-Label Data Using Weighted Penalized Empirical Risk Minimization. Int. J. Appl. Math. Comput. Sci. 32 (2) (2022)
Mariusz Kubkowski, Jan Mielniczuk, Pawel Teisseyre
How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information. J. Mach. Learn. Res. 22 (2021)
Pawel Teisseyre
Classifier chains for positive unlabelled multi-label learning. Knowl. Based Syst. 213 (2021)
Jan Mielniczuk, Pawel Teisseyre
Detection of Conditional Dependence Between Multiple Variables Using Multiinformation. ICCS (6) (2021)
Pawel Teisseyre, Tomasz Klonecki
Controlling Costs in Feature Selection: Information Theoretic Approach. ICCS (2) (2021)
Malgorzata Lazecka, Jan Mielniczuk, Pawel Teisseyre
Estimating the class prior for positive and unlabelled data via logistic regression. Adv. Data Anal. Classif. 15 (4) (2021)
Pawel Teisseyre, Jan Mielniczuk, Michal J. Dabrowski
Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique. ICCS (3) (2020)
Pawel Teisseyre, Jan Mielniczuk, Malgorzata Lazecka
Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data. ICCS (4) (2020)
Pawel Teisseyre
Learning Classifier Chains Using Matrix Regularization: Application to Multimorbidity Prediction. ECAI (2020)
Jan Mielniczuk, Pawel Teisseyre
Stopping rules for mutual information-based feature selection. Neurocomputing 358 (2019)
Pawel Teisseyre, Damien Zufferey, Marta Slomka
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification. Pattern Recognit. 86 (2019)
Mateusz Pawluk, Pawel Teisseyre, Jan Mielniczuk
Information-Theoretic Feature Selection Using High-Order Interactions. LOD (2018)
Marcin Sydow, Katarzyna Baraniak, Pawel Teisseyre
Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia. J. Intell. Inf. Syst. 48 (3) (2017)
Pawel Teisseyre
CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization. Neurocomputing 235 (2017)
Pawel Teisseyre, Robert A. Klopotek, Jan Mielniczuk
Random Subspace Method for high-dimensional regression with the R package regRSM. Comput. Stat. 31 (3) (2016)
Pawel Teisseyre
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning. CoRR (2016)
Pawel Teisseyre
Feature ranking for multi-label classification using Markov Networks. CoRR (2016)
Pawel Teisseyre
Feature ranking for multi-label classification using Markov networks. Neurocomputing 205 (2016)
Piotr Przybyla, Pawel Teisseyre
What Do Your Look-alikes Say about You? Exploiting Strong and Weak Similarities for Author Profiling. CLEF (Working Notes) (2015)
Jan Mielniczuk, Pawel Teisseyre
Using random subspace method for prediction and variable importance assessment in linear regression. Comput. Stat. Data Anal. 71 (2014)
Piotr Przybyla, Pawel Teisseyre
Analysing Utterances in Polish Parliament to Predict Speaker's Background. J. Quant. Linguistics 21 (4) (2014)
Jan Mielniczuk, Pawel Teisseyre
Model Selection in Logistic Regression Using p-Values and Greedy Search. SIIS (2011)