BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets

Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the...
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Autor*in:	Devi Priya, R [verfasserIn] Kuppuswami, S [verfasserIn] Sivaraj, R [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Non-ignorable missing data Bayesian techniques genetic algorithm Bayesian genetic algorithm continuous attributes discrete attributes

Übergeordnetes Werk:	Enthalten in: Sādhāna - Bangalore : Acad., 1978, 41(2016), 8 vom: Aug., Seite 825-836
Übergeordnetes Werk:	volume:41 ; year:2016 ; number:8 ; month:08 ; pages:825-836

Links:	Volltext

DOI / URN:	10.1007/s12046-016-0526-3

Katalog-ID:	SPR024123706

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520			\|a Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.
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912			\|a GBV_ILN_105
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912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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912			\|a GBV_ILN_250
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912			\|a GBV_ILN_2005
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912			\|a GBV_ILN_2015
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912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
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912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
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912			\|a GBV_ILN_2059
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912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
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912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
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allfields	10.1007/s12046-016-0526-3 doi (DE-627)SPR024123706 (SPR)s12046-016-0526-3-e DE-627 ger DE-627 rakwb eng 600 ASE 50.00 bkl Devi Priya, R verfasserin aut BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%. Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213 Kuppuswami, S verfasserin aut Sivaraj, R verfasserin aut Enthalten in Sādhāna Bangalore : Acad., 1978 41(2016), 8 vom: Aug., Seite 825-836 (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:41 year:2016 number:8 month:08 pages:825-836 https://dx.doi.org/10.1007/s12046-016-0526-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 50.00 ASE AR 41 2016 8 08 825-836
spelling	10.1007/s12046-016-0526-3 doi (DE-627)SPR024123706 (SPR)s12046-016-0526-3-e DE-627 ger DE-627 rakwb eng 600 ASE 50.00 bkl Devi Priya, R verfasserin aut BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%. Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213 Kuppuswami, S verfasserin aut Sivaraj, R verfasserin aut Enthalten in Sādhāna Bangalore : Acad., 1978 41(2016), 8 vom: Aug., Seite 825-836 (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:41 year:2016 number:8 month:08 pages:825-836 https://dx.doi.org/10.1007/s12046-016-0526-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 50.00 ASE AR 41 2016 8 08 825-836
allfields_unstemmed	10.1007/s12046-016-0526-3 doi (DE-627)SPR024123706 (SPR)s12046-016-0526-3-e DE-627 ger DE-627 rakwb eng 600 ASE 50.00 bkl Devi Priya, R verfasserin aut BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%. Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213 Kuppuswami, S verfasserin aut Sivaraj, R verfasserin aut Enthalten in Sādhāna Bangalore : Acad., 1978 41(2016), 8 vom: Aug., Seite 825-836 (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:41 year:2016 number:8 month:08 pages:825-836 https://dx.doi.org/10.1007/s12046-016-0526-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 50.00 ASE AR 41 2016 8 08 825-836
allfieldsGer	10.1007/s12046-016-0526-3 doi (DE-627)SPR024123706 (SPR)s12046-016-0526-3-e DE-627 ger DE-627 rakwb eng 600 ASE 50.00 bkl Devi Priya, R verfasserin aut BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%. Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213 Kuppuswami, S verfasserin aut Sivaraj, R verfasserin aut Enthalten in Sādhāna Bangalore : Acad., 1978 41(2016), 8 vom: Aug., Seite 825-836 (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:41 year:2016 number:8 month:08 pages:825-836 https://dx.doi.org/10.1007/s12046-016-0526-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 50.00 ASE AR 41 2016 8 08 825-836
allfieldsSound	10.1007/s12046-016-0526-3 doi (DE-627)SPR024123706 (SPR)s12046-016-0526-3-e DE-627 ger DE-627 rakwb eng 600 ASE 50.00 bkl Devi Priya, R verfasserin aut BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%. Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213 Kuppuswami, S verfasserin aut Sivaraj, R verfasserin aut Enthalten in Sādhāna Bangalore : Acad., 1978 41(2016), 8 vom: Aug., Seite 825-836 (DE-627)359574963 (DE-600)2097680-X 0973-7677 nnns volume:41 year:2016 number:8 month:08 pages:825-836 https://dx.doi.org/10.1007/s12046-016-0526-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 50.00 ASE AR 41 2016 8 08 825-836
language	English
source	Enthalten in Sādhāna 41(2016), 8 vom: Aug., Seite 825-836 volume:41 year:2016 number:8 month:08 pages:825-836
sourceStr	Enthalten in Sādhāna 41(2016), 8 vom: Aug., Seite 825-836 volume:41 year:2016 number:8 month:08 pages:825-836
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topic_facet	Non-ignorable missing data Bayesian techniques genetic algorithm Bayesian genetic algorithm continuous attributes discrete attributes
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container_title	Sādhāna
authorswithroles_txt_mv	Devi Priya, R @@aut@@ Kuppuswami, S @@aut@@ Sivaraj, R @@aut@@
publishDateDaySort_date	2016-08-01T00:00:00Z
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author	Devi Priya, R
spellingShingle	Devi Priya, R ddc 600 bkl 50.00 misc Non-ignorable missing data misc Bayesian techniques misc genetic algorithm misc Bayesian genetic algorithm misc continuous attributes misc discrete attributes BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets
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topic_title	600 ASE 50.00 bkl BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets Non-ignorable missing data (dpeaa)DE-He213 Bayesian techniques (dpeaa)DE-He213 genetic algorithm (dpeaa)DE-He213 Bayesian genetic algorithm (dpeaa)DE-He213 continuous attributes (dpeaa)DE-He213 discrete attributes (dpeaa)DE-He213
topic	ddc 600 bkl 50.00 misc Non-ignorable missing data misc Bayesian techniques misc genetic algorithm misc Bayesian genetic algorithm misc continuous attributes misc discrete attributes
topic_unstemmed	ddc 600 bkl 50.00 misc Non-ignorable missing data misc Bayesian techniques misc genetic algorithm misc Bayesian genetic algorithm misc continuous attributes misc discrete attributes
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title	BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets
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title_full	BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets
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title_sort	bagel: a non-ignorable missing value estimation method for mixed attribute datasets
title_auth	BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets
abstract	Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.
abstractGer	Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.
abstract_unstemmed	Abstract Surveys are mainly conducted to obtain valuable information on some criteria from a specified population. But, the survey results often become biased due to non-response of the subjects under study for highly significant attributes. Such non-ignorable missingness need to be treated and the actual values should be retrieved. Many methods have already been proposed for handling missing values in either discrete or continuous attributes. But, there exists a large gap in handling non-ignorable missing values in datasets with mixed attributes. With the intent of addressing this gap, this paper proposes a methodology called as Bayesian Genetic Algorithm (BAGEL) with hybridized Bayesian and Genetic Algorithm principles. In BAGEL, the initial population is generated using Bayesian model and fitness values of the chromosomes are evaluated using Bayesian principles. BAGEL is implemented in real datasets for imputing both discrete and continuous missing values and the imputation accuracy is observed. The experimental results show the superior performance of BAGEL than other standard imputation techniques. Statistical tests conducted to validate the experimental results also prove that BAGEL outperforms at all missing rates from 5% to 50%.
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container_issue	8
title_short	BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets
url	https://dx.doi.org/10.1007/s12046-016-0526-3
remote_bool	true
author2	Kuppuswami, S Sivaraj, R
author2Str	Kuppuswami, S Sivaraj, R
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doi_str	10.1007/s12046-016-0526-3
up_date	2024-07-03T23:33:11.101Z
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BAGEL: A non-ignorable missing value estimation method for mixed attribute datasets

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