Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
Wright State University / OhioLINK
2021
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=wright1620719735415272 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-wright1620719735415272 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-wright16207197354152722021-08-03T07:17:27Z Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods Skapura, Nicholas Artificial Intelligence Computer Science classifier machine learning pattern contrast pattern classifier weakness learning algorithm Classification is an important branch of machine learning that impacts many areas of modern life. Many classification algorithms (classifiers for short) have been developed. They have highly different levels of sophistication and classification accuracy. Classification problems often have highly different levels of hardness and complexity. Practitioners of classification modeling need better understanding of those algorithms in order to select the optimal algorithm for given classification problems. Researchers of classification need new insight on how given classifiers are weak and how they can be improved by correcting their classification errors. This dissertation introduces new tools and concepts to analyze classifier weakness and provides new insights on classifier weakness and classifier error correctability. Three tools are introduced to discover such insights. (i) The primary tool is a novel algorithm called Pattern-Aided Mixed-Type Modeling (PAMM). This tool produces a structural model revealing the shape and structure of a classifier’s error space, hence offering new analytical possibilities. (ii) Based on the structured model thus produced, new weakness metrics are introduced, incorporating structural properties of the error space and the correctability of classification errors. (iii) This study uses Corrective Method Sets (CMS), which are sets of popular, simple classifiers, to characterize a classifier’s weakness based on how much of a classifier’s errors can be corrected by the CMS.Two families of valuable insights on 11 popular classifiers are obtained using the three new tools. (i) The 11 popular classifiers are ranked in terms of how structured their error spaces are and how correctable their classification errors are, giving insights into classifier weakness and correctability. Such rankings are also compared against pure accuracy-based classifier rankings, giving insights on the relationship between poor classifier accuracy and classifier error correctability. (ii) The top ranked CMS of three types are provided: those applicable to all 11 classifiers, those applicable to each given classifier, and those applicable to each given classifier on data sets with certain characteristics. In summary, this dissertation offers insights on how many opportunities classifiers leave on the table and how much their classification errors can be easily corrected using simple corrective methods. 2021-05-18 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1620719735415272 http://rave.ohiolink.edu/etdc/view?acc_num=wright1620719735415272 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Artificial Intelligence Computer Science classifier machine learning pattern contrast pattern classifier weakness learning algorithm |
| spellingShingle |
Artificial Intelligence Computer Science classifier machine learning pattern contrast pattern classifier weakness learning algorithm Skapura, Nicholas Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| author |
Skapura, Nicholas |
| author_facet |
Skapura, Nicholas |
| author_sort |
Skapura, Nicholas |
| title |
Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| title_short |
Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| title_full |
Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| title_fullStr |
Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| title_full_unstemmed |
Analysis of Classifier Weaknesses Based on Patterns and Corrective Methods |
| title_sort |
analysis of classifier weaknesses based on patterns and corrective methods |
| publisher |
Wright State University / OhioLINK |
| publishDate |
2021 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=wright1620719735415272 |
| work_keys_str_mv |
AT skapuranicholas analysisofclassifierweaknessesbasedonpatternsandcorrectivemethods |
| _version_ |
1719458595669540864 |