Firefly inspired feature selection for face recognition

Agarwal, Vandana; Bhanot, Surekha

doi:10.1109/ic3.2015.7346689

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…The features are extracted using a Discrete Cosine Transform (DCT) and Haar wavelets based Discrete Wavelet Transform (DWT). The algorithm is validated using benchmark face databases, namely ORL and Yale while outperforming various existing techniques [31]. Classification-based feature selection using firefly algorithm and fuzzy entropy, to increase the accuracy and performance of glistening's detection with the basic k-Nearest Classifier using all features and compared the result with feature selection method using standard firefly algorithm by evaluating average accuracy, precision, and F-measure, against ophthalmologist's hand-drawn ground-truth [32].…”

Section: Firefly Algorithm-based Feature Selectionmentioning

confidence: 99%

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao¹

2019

IJATCSE

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Academic database is considered as the heart and soul of every higher education institutions. This database contains a vast amount of useful information that is useful for analysis. Algorithms for machine learning play a significant role in mining academic databases and have been proven to be effective when applied in the academic field. Prediction models are made using relevant classification algorithms for dropout analysis. The success of the prediction model depends on the performance of the feature selection algorithm used for dimensionality reduction. The study utilized the Modified Mutated Firefly Algorithm (MMFA) as a dimensionality reduction strategy to enhance the accuracy of the prediction model for dropout analysis. The results of the simulation revealed that the Decision Tree (DT) classifier outperformed the Naïve Bayesian using the three UCI datasets. After the test of benchmark datasets, a students' cumulative dataset was used to come up with a predictive model for dropout analysis of Davao del Norte State College, Davao del Norte, Philippines. The results of the experiment confirmed that the MMFA+DT obtained an accuracy rate of 95.82%, while MMFA+NB only has 92.85% using 10-fold cross-validation.

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Section: Firefly Algorithm-based Feature Selectionmentioning

confidence: 99%

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao¹

2019

IJATCSE

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“…Meta-heuristic methods such as swarm intelligence [18] algorithms generate random solutions and achieve promising results within less time [19]. Swarm intelligence methods have been used widely for enhancing the FS process, such as face recognition [20], machine scheduling [21], medical diagnosis [17,22], multi-objective power scheduling [23] and software defect prediction [24].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

et al. 2021

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Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.

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