Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm

Venkatasalam, K.; Rajendran, P.; Thangavel, M.

doi:10.1007/s10916-019-1200-1

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Pooling layer: "In the CNN, pooling layers conduct downsampling operations with the data obtained from the convolutional layers." The value of is computed as K in Equation (31) for each pooling function pool (•) connected to act l ,,c .…”

Section: Initialize Populationmentioning

confidence: 99%

“…In 2019, Venkatasalam et al 31 have developed a new lightweight mechanism for solving the issues faced during the optimal selection of the features in big data. The accelerated flower pollination (AFP) algorithm was utilized by the authors to select the appropriate features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…But, here the misclassification in data is higher and so the consistency seems to be lower. Further, the PFBP 31 has linear scalability and better classification accuracy. Here, the features seem to be highly correlated, and therefore the FS is not said to be more reliable.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Here, the features seem to be highly correlated, and therefore the FS is not said to be more reliable. The AFP Algorithm in Reference 31 consumes lower time while selecting the features. However, the preprocessing operation is poor.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

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Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.

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Section: Initialize Populationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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