Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble
Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to prepr...
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| Published in | Computational and mathematical methods in medicine Vol. 2021; pp. 1 - 12 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
26.04.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1748-670X 1748-6718 1748-6718 |
| DOI | 10.1155/2021/5556992 |
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| Abstract | Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research. |
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| AbstractList | Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM),
K
-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research. Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research. Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K-nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research. Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research. |
| Author | Ye, Mingquan Wu, Changrong Xiong, Yueling |
| AuthorAffiliation | 1 School of Medical Information, Wannan Medical College, Wuhu 241002, China 2 School of Computer and Information, Anhui Normal University, Wuhu 241002, China |
| AuthorAffiliation_xml | – name: 2 School of Computer and Information, Anhui Normal University, Wuhu 241002, China – name: 1 School of Medical Information, Wannan Medical College, Wuhu 241002, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33986823$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.chinastron.2020.08.005 10.1093/bioinformatics/16.10.906 10.1016/j.asoc.2019.01.015 10.1007/s00500-019-03879-7 10.5958/0976-5506.2019.04356.0 10.1016/j.bspc.2019.01.012 10.1162/coli_a_00323 10.1016/j.eswa.2017.05.033 10.1007/s10853-017-1252-x 10.2174/2213275912666190101121058 10.1016/j.knosys.2013.02.008 10.1016/j.chemolab.2017.12.014 10.1016/j.neucom.2020.01.101 10.3233/JIFS-181665 10.1016/j.engappai.2015.04.003 10.1016/j.asoc.2019.04.031 10.4258/hir.2019.25.4.283 10.1080/24699322.2019.1649074 10.5121/ijcnc.2019.11207 10.1186/s12859-020-03790-1 10.1080/19439962.2019.1579288 10.1016/j.jpdc.2019.12.015 10.1038/s41598-020-66466-z 10.1016/j.ygeno.2019.11.004 10.1016/j.ins.2019.02.062 10.1016/j.neucom.2019.07.061 10.1186/s13638-020-01800-7 10.1007/s40484-020-0226-1 10.1016/j.compeleceng.2013.11.024 10.1186/s12859-020-03731-y 10.2174/1574893614666190204150918 10.1093/bioinformatics/btz772 10.1016/j.compbiomed.2020.104089 10.1016/j.neucom.2020.07.050 10.1007/s00138-020-01094-1 10.1007/s11548-019-02016-x 10.1186/s12984-017-0255-9 10.4015/s1016237220500131 10.1038/s41598-019-53034-3 10.3390/molecules22122086 10.1002/nag.3111 10.1002/ijfe.1698 10.1166/jmihi.2016.1866 10.1504/IJIPT.2017.083033 10.1016/j.gpb.2017.08.002 10.1016/S0893-6080(05)80023-1 10.4018/IJSIR.2020010104 |
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| SubjectTerms | Algorithms Bayes Theorem Computational Biology Databases, Genetic - statistics & numerical data Decision Trees Female Gene Expression Regulation, Neoplastic Humans Machine Learning Male Neoplasms - classification Neoplasms - genetics Neural Networks, Computer Oligonucleotide Array Sequence Analysis - statistics & numerical data Oncogenes ROC Curve Support Vector Machine |
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| Title | Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble |
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