A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis

Sawhney, Ravinder Singh; Mathur, Puneet; Shankar, Ravi

doi:10.1007/978-3-319-95162-1_30

Cited by 59 publications

(28 citation statements)

References 26 publications

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“…The dual problem is a key point to derive algorithms and study their convergence properties. Since our formulation (1) is equivalent to the one in the work of Bach et al [18], they lead to the same dual problem. The Lagrangian of formulation (1) is as follows:…”

Section: Proofmentioning

confidence: 97%

“…This dual problem is difficult to optimize due to the last constraint, which may be moved to the objective function, but the latter then becomes non-differentiable causing new difficulties [18].…”

Section: Proofmentioning

confidence: 99%

“…The random forest and GA feature selection gave the highest accuracy. Sawhney et al [18] explored the inclusion of a penalty function to the existing fitness function promoting the Binary Firefly Algorithm to drastically reduce the feature set to an optimal subset, and their results showed an increase in both classification accuracy and feature reduction using a random forest classifier for the diagnosis of breast, cervical and hepatocellular carcinoma.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous multiple kernel learning for breast cancer outcome evaluation

Gong

Jiang

2020

BMC Bioinformatics

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Background: Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation. However, selecting the appropriate kernels and their parameters still needs further investigation. Results: We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL. Conclusions: On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient's condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

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Section: Proofmentioning

confidence: 97%

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous multiple kernel learning for breast cancer outcome evaluation

Gong

Jiang

2020

BMC Bioinformatics

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“…Intensity of light is proportional to fitness of insects where attraction is a comparative parameter and was assessed by the other insects. Sawhney et al [72] has developed a firefly-based wrapper feature collection technique using random forest classifiers for cancer diagnosis.…”

Section: Fa In Biomedical Engineeringmentioning

confidence: 99%

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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“…In addition, most of the rules or mathematical equations used by most of the metaheuristic methods have been inspired by the living and survival systems of insects, animals, and birds. Due to the noticeable success of metaheuristic algorithms in solving a lot of optimization problems in a wide range of applications, there are various types of metaheuristic algorithms include Genetic algorithm [21,22], Firefly Algorithm [23], Particle swarm optimization [24,25], Ant Colony Optimization [26], Bat algorithm [27], Whale Optimization Algorithm [28], Artificial fish swarm [29], and Grey wolf optimizer [30] has been extensively reported in recent literature. To classify breast tumors into cancerous and non-cancerous ones, an ensemble learning method was proposed by Vinod Jagannath Kadam et al [17] based on SoftMax Regression and Sparse Autoencoders.…”

Section: Related Workmentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi-Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT). Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT's parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT's main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc.) was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT-MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

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A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis

Cited by 59 publications

References 26 publications

Heterogeneous multiple kernel learning for breast cancer outcome evaluation

Heterogeneous multiple kernel learning for breast cancer outcome evaluation

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

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