Soft Computing in Data Science by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9789811503993
Publisher: Springer Singapore

In this hybrid FS model, first the Correlation Variable Evaluator technique filters the related features which results in reducing features subset. Then, CFS Subset Evaluator with Linear Forward Selection (LFS) searches for the most related features producing in optimum feature set applied for classifying the disease.

2.4 Bayesian Relevance Feedback

This study presents also an enhanced Bayesian model as illustrated in Fig. 2, which can adaptively enhance oral cancer diagnosis performance [8]. The main idea of this research is utilized the probability concept and the Bayesian algorithm. The proposed model contains three components: prior, conditional and posterior probability. This study concentrates on posterior calculations to classify stages, according to the same oral cancer cases and symptoms. The process of learning in a relevance feedback is adapted in the posterior computation. The relevant feedback process starts when the system classifies a number of new objects by using existing parameters. Since the classification of the object may be correct or incorrect hence, knowledge experts will be used to monitor newly classified objects to improve classification performance and produce high quality results. At the end of each iteration, the model acquires the appropriate class as generated by the expert, and the corrected object feature combined with their class will be applied in the new iteration [8].

Fig. 2.Bayesian Relevance Feedback (BRF).

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