Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin14068109472021-08-03T06:26:29Z Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data Haning, Jacob M. Artificial Intelligence Feature Selection Ensemble Classification Relief-f CART ANOVA There are many feature selection algorithms and many classification methods available to choose from in order to successfully and accurately learn a data set. This work focuses on the merits of dimensionality reduction and the comparative analysis of select techniques. Relief-f, Classification and Regression Trees (CART), and Analysis of Variance (ANOVA) are used to select subsets of features within four real world and one artificial data set. The three are then combined to produce and ensemble feature subset. These results are used as input for feed forward artificial neural networks (FFANN), Naive Bayes, support vector machines (SVM), and an ensemble the three. Averaged accuracy percentages are used to analyze the performance of each dimensionality reduction approach. It was found that the ensemble approach to feature selection produced generally more accurate results overall. Average accuracies included 96.5% of correctly identified benign and malignant breast cancer tumors and 89.5% of appropriately labeled splice junctions within DNA sequences, while reducing the data sets from nine features and sixty features respectively to five representative features each. 2014-10-13 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406810947 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406810947 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. |
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language |
English |
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topic |
Artificial Intelligence Feature Selection Ensemble Classification Relief-f CART ANOVA |
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Artificial Intelligence Feature Selection Ensemble Classification Relief-f CART ANOVA Haning, Jacob M. Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
author |
Haning, Jacob M. |
author_facet |
Haning, Jacob M. |
author_sort |
Haning, Jacob M. |
title |
Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
title_short |
Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
title_full |
Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
title_fullStr |
Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
title_full_unstemmed |
Feature Selection for High-Dimensional Individual and Ensemble Classifiers with Limited Data |
title_sort |
feature selection for high-dimensional individual and ensemble classifiers with limited data |
publisher |
University of Cincinnati / OhioLINK |
publishDate |
2014 |
url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406810947 |
work_keys_str_mv |
AT haningjacobm featureselectionforhighdimensionalindividualandensembleclassifierswithlimiteddata |
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1719437029329076224 |