KARMA VERİ İKİLİLEŞTİRME YÖNTEMİ İLE YENİ BİR RASYONEL SINIFLANDIRMA YAKLAŞIMI

Year 2023, Volume: 11 Issue: 4, 1257 - 1269, 30.12.2023

Muhammed Sütçü İbrahim Tümay Gülbahar

https://doi.org/10.21923/jesd.1121792

Abstract

Sınıflandırma algoritması, yeni gözlemlerin kategorisini belirlemek için kullanılan denetimli bir öğrenme tekniğidir. Ancak bazı durumlarda nicel ve nitel verilerin birlikte kullanılması gerekir. Bu yaklaşımla nicel ve nitel verilerin birlikte kullanılmasında karşılaşılan sorunlar aşılmaya çalışılmıştır. Bu çalışmada, gerçek dünyada veriler ikili, sayısal veya kategorik gibi farklı türlerde sınıflandırıldığından, tüm veri türlerini ikili verilere dönüştürerek yeni bir sınıflandırma tekniği modellenmektedir. Bu sayede çok özellikli veri sınıflandırma problemleri için daha doğru ve verimli bir karma veri ikilileştirme yaklaşımı geliştirilmiştir. Öncelikle mevcut veri setinden sınıfları belirlenmektedir ve ardından yeni önerilen veri ikilileştirme yaklaşımını kullanarak yeni örnekleri bu önceden belirlenmiş sınıflara sınıflandırılmaktadır. Bu algoritmanın her adımının nasıl verimli bir şekilde gerçekleştirilebileceğini sayısal bir örnekle gösterilmiştir. Ardından, önerilen yaklaşımı iyi bilinen bir iris veri kümesine uygulamış ve modelimiz önceki yaklaşımlara göre umut verici sonuçlar ve iyileştirmeler verdiği gösterilmiştir.

Keywords

İkilileştirme, Sınıflandırma, Veri Madenciliği, IRIS Veri Seti, Karar Modelleri.

A NEW RATIONAL CLASSIFICATION APPROACH BY THE NEW MIXED DATA BINARIZATION METHOD

Abstract

Classification algorithm is a supervised learning technique that is used to identify the category of new observations. However, in some cases, quantitative and qualitative data must be used together. With this approach, we tried to overcome the problems encountered in using quantitative and qualitative data together. In this paper, we model a new classification technique by converting all types of data to binary data because in real world, data are classified in different types such as binary, numeric or categorical. By this way, we develop a more accurate and efficient mixed data binarization approach for multi-attribute data classification problems. First, we determine the classes from available dataset and then we classify the new instances into these predetermined classes by using the new proposed data binarization approach. We show how each step of this algorithm could be performed efficiently with a numeric example. Then, we apply the proposed approach on a well-known iris dataset and our model show promising results and improvements over previous approaches.

Keywords

Binarization, Classification, Data Mining, IRIS Data Set, Decision Models

References

• Bai, Jing, Anran Yuan, Zhu Xiao, Huaji Zhou, Dingchen Wang, Hongbo Jiang, and Licheng Jiao. 2020. “Class Incremental Learning With Few-Shots Based on Linear Programming for Hyperspectral Image Classification.” IEEE Transactions on Cybernetics.
• Buniyamin, Norlida, Usamah bin Mat, and Pauziah Mohd Arshad. 2016. “Educational Data Mining for Prediction and Classification of Engineering Students Achievement.” In 2015 IEEE 7th International Conference on Engineering Education, ICEED 2015, 49–53. Institute of Electrical and Electronics Engineers Inc.
• Carnevalli, Jose A., and Paulo Cauchick Miguel. 2008. “Review, Analysis and Classification of the Literature on QFD-Types of Research, Difficulties and Benefits.” International Journal of Production Economics 114 (2): 737–54.
• Cover, T. M., and P. E. Hart. 1967. “Nearest Neighbor Pattern Classification.” IEEE Transactions on Information Theory 13 (1): 21–27.
• Faes, L, M K Schmid, S K Wagner Bmbch, Liu Mbchb, R Chopra Bsc, N Pontikos, Sim Phd, et al. 2019. “Automated Deep Learning Design for Medical Image Classification by Health-Care Professionals with No Coding Experience: A Feasibility Study.” Articles Lancet Digital Health 1: 232–74.
• Fisher. 1988. “Iris Data Set.” UCI Center for Machine Learning and Intelligent Systems. July 1, 1988. https://archive.ics.uci.edu/ml/datasets/Iris.
• FISHER, R. A. 1936. “THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS.” The Annals of Human Genetics, September, 179–88.
• Graur, Dan, Yichen Zheng, and Ricardo B.R. Azevedo. 2015. “An Evolutionary Classification of Genomic Function.” Genome Biology and Evolution 7 (3): 642–45. https://doi.org/10.1093/gbe/evv021.
• Jouini, Mouna, Latifa Ben Arfa Rabai, and Anis ben Aissa. 2014. “Classification of Security Threats in Information Systems.” In Procedia Computer Science, 32:489–96. Elsevier B.V.
• Loh, Wei Yin. 2011. “Classification and Regression Trees.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 (1): 14–23.
• Melin, Patricia, Frumen Olivas, Oscar Castillo, Fevrier Valdez, Jose Soria, and Mario Valdez. 2013. “Optimal Design of Fuzzy Classification Systems Using PSO with Dynamic Parameter Adaptation through Fuzzy Logic.” Expert Systems with Applications 40 (8): 3196–3206.
• Pal, Mahesh, and Giles M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2297–2307.
• Pratikakis, I, F Dupont, and M Ovsjanikov. 2017. “Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval.” Eurographics Workshop on 3D Object Retrieval.
• Russo, Mohammad Ashraf, Laksono Kurnianggoro, and Kang-Hyun Jo. 2019. 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE.
• Schwenker, Friedhelm, and Edmondo Trentin. 2014. “Pattern Classification and Clustering: A Review of Partially Supervised Learning Approaches.” Pattern Recognition Letters 37 (1): 4–14.
• Silva, Thiago Christiano, and Liang Zhao. 2012. “Network-Based High Level Data Classification.” IEEE Transactions on Neural Networks and Learning Systems 23 (6): 954–70.
• Singhal, Piyush, Gopal Agarwal, and Murali Lal Mittal. 2011. “Supply Chain Risk Management: Review, Classification and Future Research Directions.” Journal of Business Science and Applied Management. Vol. 6.
• Sutcu, Muhammed. 2020. “Effects of Total Cost of Ownership on Automobile Purchasing Decisions.” Transportation Letters 12 (1): 18–24.
• UC Irvine. 2007. “UCI Machine Learning Repository.” UCI. 2007. https://archive.ics.uci.edu/ml/datasets.php. University of Toronto. 2003. “Data for Evaluating Learning in Valid Experiments.” 2003. https://www.cs.toronto.edu/~delve/.
• Waltman, Ludo, and Nees Jan van Eck. 2012. “A New Methodology for Constructing a Publication-Level Classification System of Science.” Journal of the American Society for Information Science and Technology 63 (12): 2378–92.
• Zhang, Liang, Lingling Zhang, Weili Teng, and Yibing Chen. 2013. “Based on Information Fusion Technique with Data Mining in the Application of Finance Early-Warning.” In Procedia Computer Science, 17:695–703. Elsevier B.V.
• Zhang, Shichao, Xuelong Li, Ming Zong, Xiaofeng Zhu, and Ruili Wang. 2018. “Efficient KNN Classification with Different Numbers of Nearest Neighbors.” IEEE Transactions on Neural Networks and Learning Systems 29 (5): 1774–85.