Learning Capabilities of AI Methodologies on Multi-Class Datasets

Öz

Machine Learning (ML) methods have numerous kinds of application areas up to now. Since they generally have remarkable success in learning, study areas and research field have diversified drastically. Neural networks seem to be appropriate for such a learning capability. The study discusses and examines several ML methodologies to decide the output. Since binary classification is another interesting area, the study focuses on multi-class classification problems. Datasets are chosen from a commonly known and accepted repository to avoid fakeness. Totally four different classifiers have been used to understand and know the different output classes in four different datasets. The classifiers use various arguments to work with and these will be shown and explained in detail. Two of the datasets are newly added and medium-sized, this is preferred to show that there is almost no time of execution difference among all. The system developed gives remarkable success rates and eliminates the differences among the classes using a neural networks system. It is believed that ML methods will have a wide range of application fields as researchers widen their point of view for academic studies.

Anahtar Kelimeler

• machine learning
• multi-class classification
• algorithm classifiers

Kaynakça

1. [1] Sevinç, Ender. "An empowered AdaBoost algorithm implementation: A COVID-19 dataset study." Computers & Industrial Engineering (2022): 107912.
2. [2] Mirjalili, Seyedali. "Genetic algorithm." Evolutionary algorithms and neural networks. Springer, Cham, 2019. 43-55.
3. [3] Karakaya, Murat, and Ender SEVİNÇ. "An efficient genetic algorithm for routing multiple uavs under flight range and service time window constraints." Bilişim Teknolojileri Dergisi 10.1 (2017): 113.
4. [4] Cingil, Ibrahim, et al. "Dynamic modification of XML documents: External application invocation from XML." ACM SIGecom exchanges 1.1 (2000): 1-6.
5. [5] Sevinc, Ender. "A novel evolutionary algorithm for data classification problem with extreme learning machines." IEEE Access 7 (2019): 122419-122427.
6. [6] Dokeroglu, Tansel, and Ender Sevinc. "Memetic Teaching–Learning-Based Optimization algorithms for large graph coloring problems." Engineering Applications of Artificial Intelligence 102 (2021): 104282.
7. [7] Sevinc, Ender, and Tansel Dokeroglu. "A novel parallel local search algorithm for the maximum vertex weight clique problem in large graphs." Soft Computing 24.5 (2020): 3551-3567.
8. [8] Too, Jingwei, and Seyedali Mirjalili. "A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study." Knowledge-Based Systems 212 (2021): 106553.

9. [9] Quinlan, J. Ross. "Induction of decision trees." Machine learning 1.1 (1986): 81-106.
10. [10] Pisner, Derek A., and David M. Schnyer. "Support vector machine." Machine learning. Academic Press, 2020. 101-121.
11. [11] Rish, Irina. "An empirical study of the naive Bayes classifier." IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol. 3. No. 22. 2001.
12. [12] UC Irvine Machine Learning Repository [Online]. Available: https://archive.ics.uci.edu/ml/datasets.php [Accessed: 10-Apr-2022].