| THE EFFECT OF MISLABELED SAMPLES ON THE PERFORMANCE OF THE LINEAR LEARNING MACHINE |
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| 引用本文： | BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN Department of Chemistry,Clarkson University,Potsdam,NY,U.S.A.ROY-KEITH SMITH Georgia Department of Agriculture,Capitol Square,Atlant,GA,U.S.A.ORLEY R.TAYLOR Department of Entomology,University of Kansas,Lawrence,KS,U.S.A.. THE EFFECT OF MISLABELED SAMPLES ON THE PERFORMANCE OF THE LINEAR LEARNING MACHINE[J]. 地理学报(英文版), 1990, 0(1) |
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| 作者姓名： | BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN Department of Chemistry Clarkson University Potsdam NY U.S.A.ROY-KEITH SMITH Georgia Department of Agriculture Capitol Square Atlant GA U.S.A.ORLEY R.TAYLOR Department of Entomology University of Kansas Lawrence KS U.S.A. |
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| 作者单位： | BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN Department of Chemistry，Clarkson University，Potsdam，NY 13676，U.S.A.ROY-KEITH SMITH Georgia Department of Agriculture，Capitol Square，Atlanta，GA 30334，U.S.A.ORLEY R.TAYLOR Department of Entomology，University of Kansas，Lawrence，KS 66045，U.S.A. |
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| 摘 要： | Over the past 15 years the linear learning machine has been applied to a large number of chemicalproblems.The learning machine approach is conceptually simple and does not require knowledge aboutthe statistical distribution of the data.However,there are problems associated with this approach.Oneproblem which has not been investigated is the influence of mislabeled samples on the positioning of thehyerplane in feature space.If a few samples in a data set are incorrectly tagged prior to training(i.e.thesamples are labeled as members of class 2 even though they are actually members of class 1),it is stilIpossible using the linear learning machine to achieve a classification success rate of 100% for the trainingset.However,unfavorable results will be obtained for the prediction set.The magnitude of this effect andits potential implications regarding the proper use of the linear learning machine are discussed.
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THE EFFECT OF MISLABELED SAMPLES ON THE PERFORMANCE OF THE LINEAR LEARNING MACHINE |
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| BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN. THE EFFECT OF MISLABELED SAMPLES ON THE PERFORMANCE OF THE LINEAR LEARNING MACHINE[J]. Journal of Geographical Sciences, 1990, 0(1) |
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| Authors: | BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN |
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| Affiliation: | BARRY K.LAVINE ANTHONY J.I.WARD JIAN HWA HAN Department of Chemistry,Clarkson University,Potsdam,NY,U.S.A.ROY-KEITH SMITH Georgia Department of Agriculture,Capitol Square,Atlant,GA,U.S.A.ORLEY R.TAYLOR Department of Entomology,University of Kansas,Lawrence,KS,U.S.A. |
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| Abstract: | Over the past 15 years the linear learning machine has been applied to a large number of chemical problems.The learning machine approach is conceptually simple and does not require knowledge about the statistical distribution of the data.However,there are problems associated with this approach.One problem which has not been investigated is the influence of mislabeled samples on the positioning of the hyerplane in feature space.If a few samples in a data set are incorrectly tagged prior to training(i.e.the samples are labeled as members of class 2 even though they are actually members of class 1),it is stilI possible using the linear learning machine to achieve a classification success rate of 100% for the training set.However,unfavorable results will be obtained for the prediction set.The magnitude of this effect and its potential implications regarding the proper use of the linear learning machine are discussed. |
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| Keywords: | Classification Pattern recognition Preprocessing |
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