| Type of Material: | Thesis |
| Title: | Unbalanced data classification using genetic programming |
| Researcher: | Kumar, Arvind |
| Guide: | Goel, Shivani |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2022 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 14:51:46 | |
| 008 | __ | 261014t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464158 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aKumar, Arvind |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aUnbalanced data classification using genetic programming |
| 260 | __ | |aGreater Noida|c2022|bBennett University |
| 300 | __ | |a89p. |
| 502 | __ | |d2022|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2022|oDate of Award |
| 518 | __ | |d2018|oDate of Registration |
| 518 | __ | |oDate of Completion|d2022 |
| 520 | __ | |aIn many real-world classification applications, such as medical diagnosis, fraud detection, bioinformatics, or fault diagnostics, it is common that one class has only a limited number of training instances (called the minority class), while the other class (called the majority class) conceive the rest. Such types of data sets are called unbalanced. In data classification, machine learning (ML) methods can face a performance bias when the nature of data sets is unbalanced. In this case, the trained classifiers may have good accuracy on the majority class but lower accuracy on the minority class. Genetic Programming (GP) is an optimistic machine learning method based on the Darwinian theory of evolution to automatically emerge computer programs to solve problems without any domain-specific knowledge. Although GP has revealed much success in developing reliable and precise classifiers for typical classification jobs, GP, like many other ML algorithms, can produce biased classifiers when the nature of data is u |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGoel, Shivani|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/557668|yShodhganga |
| 905 | __ | |afromsg |
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