ArticleJournal of medical systems2019
Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
In recent times, the main problem associated with big data analytics is its high dimensional data over the search space. Such data gathers continuously in search space making traditional algorithms infeasible for data mining in real time environment. Hence, feature selection is an important method to lighten the load during processing while inducing a model for mining. However, mining over such high dimensional data leads to formulation of optimal feature subset, which grows exponentially and leads to intractable computational demand. In this paper, a novel lightweight mechanism is used as a feature selection method, which solves the after effects arising with optimal feature selection. The feature selection in big data mining is done using accelerated flower pollination (AFP) algorithm. This method improves the accuracy of feature selection with reduced processing time. The proposed method is tested under larger set of data with high dimensionality to test the performance of proposed method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.