Robust fuzzy clustering for multiple instance regression
Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the pr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Trabelsi, Mohamed [verfasserIn] Frigui, Hichem [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 90 |
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| Übergeordnetes Werk: |
volume:90 |
| DOI / URN: |
10.1016/j.patcog.2019.01.030 |
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| Katalog-ID: |
ELV001785028 |
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| 520 | |a Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. | ||
| 650 | 4 | |a Multiple instance regression | |
| 650 | 4 | |a Fuzzy clustering | |
| 650 | 4 | |a Possibilistic clustering | |
| 650 | 4 | |a Multiple model regression | |
| 700 | 1 | |a Frigui, Hichem |e verfasserin |4 aut | |
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10.1016/j.patcog.2019.01.030 doi (DE-627)ELV001785028 (ELSEVIER)S0031-3203(19)30052-4 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Trabelsi, Mohamed verfasserin aut Robust fuzzy clustering for multiple instance regression 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. Multiple instance regression Fuzzy clustering Possibilistic clustering Multiple model regression Frigui, Hichem verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 90 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:90 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 90 |
| spelling |
10.1016/j.patcog.2019.01.030 doi (DE-627)ELV001785028 (ELSEVIER)S0031-3203(19)30052-4 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Trabelsi, Mohamed verfasserin aut Robust fuzzy clustering for multiple instance regression 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. Multiple instance regression Fuzzy clustering Possibilistic clustering Multiple model regression Frigui, Hichem verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 90 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:90 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 90 |
| allfields_unstemmed |
10.1016/j.patcog.2019.01.030 doi (DE-627)ELV001785028 (ELSEVIER)S0031-3203(19)30052-4 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Trabelsi, Mohamed verfasserin aut Robust fuzzy clustering for multiple instance regression 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. Multiple instance regression Fuzzy clustering Possibilistic clustering Multiple model regression Frigui, Hichem verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 90 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:90 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 90 |
| allfieldsGer |
10.1016/j.patcog.2019.01.030 doi (DE-627)ELV001785028 (ELSEVIER)S0031-3203(19)30052-4 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Trabelsi, Mohamed verfasserin aut Robust fuzzy clustering for multiple instance regression 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. Multiple instance regression Fuzzy clustering Possibilistic clustering Multiple model regression Frigui, Hichem verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 90 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:90 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 90 |
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Robust fuzzy clustering for multiple instance regression |
| abstract |
Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. |
| abstractGer |
Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. |
| abstract_unstemmed |
Multiple instance regression (MIR) operates on a collection of bags, where each bag contains many instances sharing the same real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining ones are noisy observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this paper, we show that regression models can be identified as clusters when appropriate features and distances are used. We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to obtain an initial partition where instances can belong to multiple models with various degrees. Then, it uses unconstrained possibilistic memberships to allow the initial local models to expand and converge to the global model. These memberships are also used to identify the primary instances within each bag. After clustering, the possibilistic memberships are used to identify the optimal number of regression models. We evaluate our approach on synthetic data sets generated by varying the dimensionality of the feature space, the number of instances per bag, and the noise level. We also validate the RFC-MIR using two real applications: prediction of the yearly average yield of a crop using remote sensing data; and drug activity prediction. These applications have been used consistently to validate existing MIR algorithms. We show that our approach achieves higher accuracy than existing methods. |
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| title_short |
Robust fuzzy clustering for multiple instance regression |
| remote_bool |
true |
| author2 |
Frigui, Hichem |
| author2Str |
Frigui, Hichem |
| ppnlink |
265784131 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1016/j.patcog.2019.01.030 |
| up_date |
2024-07-06T22:33:55.492Z |
| _version_ |
1803870782614405120 |
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