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Improving prediction with enhanced Distributed Memory-based Resilient Dataset Filter
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DOI：10.1186/s40537-020-00292-y.png)
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Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure is used for significant feature identification and Distributed Memory-based Resilient Dataset Filter (DMRDF) is used to eliminate duplicate reviews, which in turn improves the reliability of the product reviews. The pre-processed dataset is used for prediction of product pre-launch in the market using classifiers such as Logistic regression and Support vector machine. DMRDF method is fault-tolerant because of its resilience property and also reduces the dataset redundancy; hence, it increases the prediction accuracy of the model. The proposed model works in a distributed environment to handle a massive volume of the dataset and therefore, it is scalable. The output of this feature modelling and prediction allows the manufacturer to optimize the design of his new product.
Keyword:
Distributed Memory-based
Resilient Distribution Dataset
Redundancy
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6.4
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1.5K
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1.1W
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