- author = "Arvind Kumar",
- title = "Unbalanced data classification Using genetic programming",
- school = "School of Computer Science Engineering and Technology (SCSET), Bennett University",
- year = "2022",
- address = "Plot Nos 8-11, Tech Zone II, Greater Noida-201310, Uttar Pradesh, India",
- month = jun,
- keywords = "genetic algorithms, genetic programming, SVM, KNN, UCI, ABL-9-18, Yeast-2, Software Engineering",
-
URL = "
http://lrcdrs.bennett.edu.in/handle/123456789/2016",
-
URL = "
http://lrcdrs.bennett.edu.in/bitstream/123456789/2016/1/PhD-Thesis-E18SOE822-Arvind-Final-signed.pdf",
- size = "108 pages",
-
abstract = "In 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 unbalanced. This
biasing is because traditional training standards such
as the overall success rate in the fitness function in
GP can be influenced by the more significant number of
instances from the majority class.
This research focuses on algorithmic methods assuming that the whole training data is important and valuable, and no data sample should be removed from the training process. The second consideration in this work is that the proposed methods should be problem-independent, and they should not expect any a-priori domain-specific or expert knowledge. Thus, this research focuses on developing GP-based approaches for unbalanced data-set classification, based on internal cost alteration in the GP fitness function and facilitating the unbalanced data set to be used “as is” in the training process. This research work demonstrates that by designing various methods in GP, we can evolve classifiers with good classification performance on the majority and the minority classes. These developed methods are evaluated, on publicly available, UCI-based binary benchmark classification problems with varying levels of imbalanced factors.",
-
notes = "E18SOE822
Supervisors: Shivani Goel and Nishant",
