FOIL BASED FEATURE SUBSET SELECTION ALGORITHM FOR PREDICTING INFERITILITY ON IVF USING CLUSTERING
This paper illustrates the concept of clustering and classification technique to identify important criteria for the infertility couples to find the success rate of In-vitro Fertilization treatment. A FOIL algorithm, First creates the construction of minimum spanning tree, after that the partition the data into each tree by clustering the similar features. Selected features are represented into clusters. At last feature interaction is done by combining the features appeared in the previous circumstances of all FOIL rules, which will achieve a candidate feature subset to avoid redundant features and reserves interactive ones. Thus, the proposed paper will determine the accuracy and efficiency of IVF treatment using R programming.
Feature selection algorithm, Data mining, Supervised filter, IVF, Spermatological data
Almuallim H, and Dietterich TG. (1992). “Algorithms for Identifying Relevant Features”. Proc. Ninth Canadian Conf. Artificial Intelligence, pp. 38-45, 1992.
Durairaj M, and Nandha RK. (2013). “Data Mining Application on IVF Data For The Selection of Influential Parameters on Fertility”, IJEAT, Vol. 2, Issue-6, pp.34-39.
Deepika S. (2018). “Eminent Feature Selection for High Dimensional Data through the Propositional Fast-Foil Rules”, IJRCS, Vol..2, Issue .1, pp.435-439.
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