Abstract
Yeast is considered one of the significant elements for medicines and a wide range of chemical products. Various types of Yeast are available. Based on certain initial characteristics (data values) the type of yeast can be ascertained. In this paper, a hybrid model has been proposed to get proper characteristics data values of yeast data so that proper type of yeast data can be ascertained. Here, 50 selected data samples among 1484 samples of yeast dataset have been taken for case study. Here at first Factor analysis (FA) and principal component analysis (PCA) has been applied to find out the cumulative effect of each sample. Then based on Residual error analysis of FA and PCA, cumulative effect value has been taken from FA and thereafter two soft computing models, viz. Particle Swarm Optimization (PSO), Fuzzy Time Series model (FTS) model and two swarm intelligence models viz. firefly algorithm and cuckoo search algorithm have been applied on that cumulative effect data. Finally, using residual analysis their performance has been evaluated.
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