Modified Flower Pollination Algorithm for Disease Identification in Swine

doi:10.22266/ijies2021.1231.55

Cited by 1 publication

(7 citation statements)

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“…Polly et al [16] introduced QIFP, an improvement from FPA [17]. The improvement lies in the step vector of global pollination and the search space technique.…”

Section: A Quadratic Interpolation Flower Pollination (Qifp)mentioning

confidence: 99%

“…The evaluation of weights for classification problems in MLP is to minimise the mean square error (MSE) value. In QIFPNN, QIFP is used as a weight adjustment [16]. The flowchart of QIFPNN can be seen in Fig.…”

Section: B Quadratic Interpolation Flower Pollination Neural Network ...mentioning

confidence: 99%

“…The QIFPNN, FPNN, PSONN, and BANN use MLP architecture with one hidden layer by determining the number of neurons in that layer according to (6) [16]. One hidden layer can approximate any function with arbitrary accuracy [19].…”

Section: B Parameter Settingsmentioning

confidence: 99%

“…However, there is no known effective strategy to produce a variety of parameters [20]. All parameters are set based on Polly et al [16], namely population [16,21],…”

Section: B Parameter Settingsmentioning

confidence: 99%

“…In recent research, Polly et al [16] looked into classifying real-world swine disease cases. The dataset comprised 158 samples, 68 attributes, a binary data type, and an unbalanced data distribution in 11 classes.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

Polly¹,

Hartati²,

Suprapto³

et al. 2023

IJACSA

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Machine Learning (ML) algorithms are widely used in solving classification problems. The biggest challenge of classification lies in the robustness of the ML algorithm in various dataset characteristics. Quadratic Interpolation Flower Pollination Neural Network (QIFPNN) is categorised into ML algorithm. The new QIFPNN's extraordinary capabilities are measured on binary-type datasets. This research ensures that the remarkable ability of QIFPNN also applies to non-binary datasets with balanced and unbalanced data class characteristics. Flower Pollination Neural Network (FPNN), Particle Swarm Optimisation Neural Network (PSONN), and Bat Neural Network (BANN) were used as comparisons. The QIFPNN, FPNN, PSONN, and BANN were used to train Multi-Layer-Perceptron (MLP). The test results on five datasets show that QIFPNN obtains an average classification accuracy higher than its comparison in three datasets with balanced and unbalanced data class characteristics. The three datasets are Iris, Wine, and Glass. The highest classification accuracy obtained by QIFPNN in the three datasets is 97.1462%, 98.6551%, and 73.1979%, respectively. Based on the F1-score test from QIFPNN, it is higher than all the comparisons in four datasets: Iris, Wine, Vertebral column, and Glass. Sequentially, 96.4599%, 98.7155%, 90.7517%, and 60.2843%. It proves that QIFPNN can also classify datasets with non-binary data types with balanced and unbalanced data class characteristics because they are more consistently tested on various datasets and are not susceptible to the influence of variations in dataset characteristics so that they can be applied to various types of data or cases.

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“…Polly et al [16] introduced QIFP, an improvement from FPA [17]. The improvement lies in the step vector of global pollination and the search space technique.…”

Section: A Quadratic Interpolation Flower Pollination (Qifp)mentioning

confidence: 99%

Section: B Quadratic Interpolation Flower Pollination Neural Network ...mentioning

confidence: 99%

Section: B Parameter Settingsmentioning

confidence: 99%

“…However, there is no known effective strategy to produce a variety of parameters [20]. All parameters are set based on Polly et al [16], namely population [16,21],…”

Section: B Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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