Classification and feature selection methods based on fitting logistic regression to PU data
In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Furmańczyk, Konrad [verfasserIn] Paczutkowski, Kacper [verfasserIn] Dudziński, Marcin [verfasserIn] Dziewa-Dawidczyk, Diana [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of computational science - Amsterdam [u.a.] : Elsevier, 2010, 72 |
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| Übergeordnetes Werk: |
volume:72 |
| DOI / URN: |
10.1016/j.jocs.2023.102095 |
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| Katalog-ID: |
ELV063191717 |
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| 245 | 1 | 0 | |a Classification and feature selection methods based on fitting logistic regression to PU data |
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| 520 | |a In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). | ||
| 650 | 4 | |a Positive unlabeled learning | |
| 650 | 4 | |a Logistic regression | |
| 650 | 4 | |a Empirical risk minimization | |
| 650 | 4 | |a Thresholded Lasso | |
| 650 | 4 | |a Mutual information-based feature selection | |
| 700 | 1 | |a Paczutkowski, Kacper |e verfasserin |0 (orcid)0000-0001-7408-6060 |4 aut | |
| 700 | 1 | |a Dudziński, Marcin |e verfasserin |0 (orcid)0000-0003-4242-8411 |4 aut | |
| 700 | 1 | |a Dziewa-Dawidczyk, Diana |e verfasserin |0 (orcid)0000-0001-9486-1685 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of computational science |d Amsterdam [u.a.] : Elsevier, 2010 |g 72 |h Online-Ressource |w (DE-627)627614825 |w (DE-600)2557360-3 |w (DE-576)324400225 |x 1877-7503 |7 nnns |
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10.1016/j.jocs.2023.102095 doi (DE-627)ELV063191717 (ELSEVIER)S1877-7503(23)00155-2 DE-627 ger DE-627 rda eng 004 VZ Furmańczyk, Konrad verfasserin (orcid)0000-0002-7683-4787 aut Classification and feature selection methods based on fitting logistic regression to PU data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). Positive unlabeled learning Logistic regression Empirical risk minimization Thresholded Lasso Mutual information-based feature selection Paczutkowski, Kacper verfasserin (orcid)0000-0001-7408-6060 aut Dudziński, Marcin verfasserin (orcid)0000-0003-4242-8411 aut Dziewa-Dawidczyk, Diana verfasserin (orcid)0000-0001-9486-1685 aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 72 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:72 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 72 |
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10.1016/j.jocs.2023.102095 doi (DE-627)ELV063191717 (ELSEVIER)S1877-7503(23)00155-2 DE-627 ger DE-627 rda eng 004 VZ Furmańczyk, Konrad verfasserin (orcid)0000-0002-7683-4787 aut Classification and feature selection methods based on fitting logistic regression to PU data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). Positive unlabeled learning Logistic regression Empirical risk minimization Thresholded Lasso Mutual information-based feature selection Paczutkowski, Kacper verfasserin (orcid)0000-0001-7408-6060 aut Dudziński, Marcin verfasserin (orcid)0000-0003-4242-8411 aut Dziewa-Dawidczyk, Diana verfasserin (orcid)0000-0001-9486-1685 aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 72 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:72 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 72 |
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10.1016/j.jocs.2023.102095 doi (DE-627)ELV063191717 (ELSEVIER)S1877-7503(23)00155-2 DE-627 ger DE-627 rda eng 004 VZ Furmańczyk, Konrad verfasserin (orcid)0000-0002-7683-4787 aut Classification and feature selection methods based on fitting logistic regression to PU data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). Positive unlabeled learning Logistic regression Empirical risk minimization Thresholded Lasso Mutual information-based feature selection Paczutkowski, Kacper verfasserin (orcid)0000-0001-7408-6060 aut Dudziński, Marcin verfasserin (orcid)0000-0003-4242-8411 aut Dziewa-Dawidczyk, Diana verfasserin (orcid)0000-0001-9486-1685 aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 72 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:72 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 72 |
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10.1016/j.jocs.2023.102095 doi (DE-627)ELV063191717 (ELSEVIER)S1877-7503(23)00155-2 DE-627 ger DE-627 rda eng 004 VZ Furmańczyk, Konrad verfasserin (orcid)0000-0002-7683-4787 aut Classification and feature selection methods based on fitting logistic regression to PU data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). Positive unlabeled learning Logistic regression Empirical risk minimization Thresholded Lasso Mutual information-based feature selection Paczutkowski, Kacper verfasserin (orcid)0000-0001-7408-6060 aut Dudziński, Marcin verfasserin (orcid)0000-0003-4242-8411 aut Dziewa-Dawidczyk, Diana verfasserin (orcid)0000-0001-9486-1685 aut Enthalten in Journal of computational science Amsterdam [u.a.] : Elsevier, 2010 72 Online-Ressource (DE-627)627614825 (DE-600)2557360-3 (DE-576)324400225 1877-7503 nnns volume:72 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 72 |
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Classification and feature selection methods based on fitting logistic regression to PU data |
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Classification and feature selection methods based on fitting logistic regression to PU data |
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Furmańczyk, Konrad |
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Furmańczyk, Konrad Paczutkowski, Kacper Dudziński, Marcin Dziewa-Dawidczyk, Diana |
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Furmańczyk, Konrad |
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10.1016/j.jocs.2023.102095 |
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classification and feature selection methods based on fitting logistic regression to pu data |
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Classification and feature selection methods based on fitting logistic regression to PU data |
| abstract |
In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). |
| abstractGer |
In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). |
| abstract_unstemmed |
In our work, we examine the classification methods where the positive and unlabeled data are considered and where the conditional distribution of the true class label given the feature vector is governed by the model of logistic regression. Our first objective is to compute and compare the selected metrics allowing for the quality assessment of these methods. In this context, we investigate four methods of the posterior probability estimation, where the risk of logistic loss function is optimized: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods – the joint approach, and the LassoJoint method. The corresponding evaluations are basically performed for 13 machine learning models on some chosen – both low- and high-dimensional – datasets. Some of the mentioned machine learning model schemes have been directly borrowed from literature and some have been obtained through some modifications in the existing procedures. Our second goal is to establish the most stable and efficient approach for the posterior probability estimation. Moreover, we use the AdaSampling scheme for comparison of the considered classification methods. We also conducted comparisons of feature selection procedures – the Mutual Information-Based feature selection method and the LassoJoint approach. The current article is an enhancement of the conference paper Furmańczyk et al. (2022). |
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Classification and feature selection methods based on fitting logistic regression to PU data |
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