Attributes Reduction in Big Data

Albattah, Waleed; Khan, Rehan Ullah; Khan, Khalil

doi:10.3390/app10144901

Cited by 4 publications

(3 citation statements)

References 36 publications

(28 reference statements)

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“…Applying the results from big data analytics is not always as easy as may be expected [2]. Several different problems can make it hard for organizations to realize the benefits promised by big data analytics vendors.…”

Section: Introductionmentioning

confidence: 99%

“…This is a problem for the majority of researchers, as they are unable to explore such huge datasets. Albattah et al [2] investigated the use of small datasets sampled from vast datasets for machine-learning models. They analyzed 40 gigabits of data using several different methods to decrease the volume of the run sets without interfering with recognition or model learning.…”

Section: Introductionmentioning

confidence: 99%

“…After analyzing several alternatives, they observed that reducing the amount of data by 50% had an insignificant impact on the performance of the machinelearning model. On average, if only half of the data are used, then for support vector machines, the reduction in performance was merely 3.6%, and for random forest, the reduction was 1.8% [2]. The 50% decrease in the data implies that, in most situations, the data can be stored in RAM and the time required to process the data is significantly lower, helping to reduce the amount of data-processing resource needed.…”

Section: Introductionmentioning

confidence: 99%

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Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

Albattah¹,

Albahli²

2023

Computer Systems Science and Engineering

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The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the Den-seNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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