TO ENHANCE THE FEATURE EXTRACTION BABC ALGORITHM TOIMPROVE THE CLASSIFICATION OF MULTIDIMENSIONAL ACCURACY
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Abstract
Presently a days people are more intrigued to express and offer their perspectives, feedback's, recommendations, and assessments about a specific point on the web. People and company rely more on online opinions about products and services for their decision making criterion-referenced tests, classification consistency and accuracy are viewed as important indicators for evaluating reliability and validity of classification results. Numerous attributes, procedures have been proposed in the framework of the unidimensional item response theory of estimate these indices. Some of these were based on total sum scores, others on latent trait estimates. However, there exist very few attempts to develop them in the framework of multidimensional item response theory. Based on previous studies, the aim of this study is first to estimate the consistency and accuracy indices of multidimensional ability estimates from a single administration of a criterion-referenced test.A noteworthy issue in distinguishing the multidimensional grouping is the high dimensional of the component extraction. The majority of these highlights are insignificant, repetitive, and loud, which influences the execution of the classifier. Along these lines, include extraction is a basic advance in the phony audit location to decrease the dimensional of the component space and to enhance precision. In this paper, double fake honey bee province (BABC) with KNN is proposed to take care of highlight extraction issue for assumption grouping. The exploratory outcomes exhibit that the proposed strategy chooses more enlightening highlights set contrasted with the aggressive strategies as it accomplishes higher characterization exactness 96.00%.
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