Abstract
This paper deals with fuzzy evolutionary algorithms which is the fast developing area in the field of machine learning and artificial intelligence. In today’s world, lots and lots of data are available for various real-life problems, where finding the relevant information from huge quantities become a challenge to the human society. This leads the way for data mining which plays an important role to extract the relevant information from vast amount of data. In this paper, deadly diseases such as lung cancer and breast cancer are considered for classification. Also, the concept of ant colony optimization (ACO) is used for variable selection, and fuzzy concept is used for classification. Based on the activities of the real ant systems, ACO is developed by Marco Dorigo in the year 1992, and it is utilized in solving many complex problems of optimization. It is one of the types of evolutionary computations. Its main aim is to identify the shortest distance between the source of food and its nest. The behavior of ants that deposits their incense on the top layer of ground to make a desired path so that other members of the colony should follow the path. Using the concept of fuzzy unordered rule induction algorithm (FURIA), classification has been done, and the results are compared with and without hybridization. Lung cancer dataset consists of 57 variables. Fuzzy gives classification accuracy as 75% with total variables. By ACO, 57 variables are reduced to five variables, and fuzzy gives classification after reduction of variables is 84.37%. Similarly, breast cancer consists of ten variables, and fuzzy classification gives accuracy as 73.07% with total variables. ACO reduced ten variables to three variables, and fuzzy gives classification after reduction of variables is 75.17%. Hence, the hybridization concept consumed less time and also less cost. By finding the relevant variables, identifying the disease at earlier stage becomes easier, and curing the disease becomes faster.
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Poongothai, S., Sharmila, S.L. (2022). A Comparison of Fuzzy and ACO-Based Fuzzy for Classification of Bio-Medical Database. In: Peng, SL., Lin, CK., Pal, S. (eds) Proceedings of 2nd International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1422. Springer, Singapore. https://doi.org/10.1007/978-981-19-0182-9_11
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