An Improved Ant Colony Optimization with Correlation and Gini Importance for Feature Selection

Joshi, Tanvi; Lahorkar, Ashwin; Tikhe, Gaurav; Bhosale, Hrushikesh; Sane, Aamod; Valadi, Jayaraman

doi:10.1007/978-981-16-1089-9_50

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results for the BH algorithm and filter ranked BH for feature selection are mentioned in Table 2. For evaluation purposes, we compared our proposed algorithm with correlation-based HACO [39] algorithm which uses Mean decrease in Gini and correlation filter methods. Performance measures for the HACO are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…In the binary HACO algorithm [39] every feature has two labels 0 and 1(selected and deselected respectively). At any iteration the move from a feature i to another feature j has four possibilities 0 − > 0, 0 − > 1, 1 − > 0 and 1 − > 1.…”

Section: Binary Hybrid Ant Colony Optimization Algorithmmentioning

confidence: 99%

“…For selecting a feature j, it employs correlation between features and while deselecting a feature j, it employs Gini importance ranking between feature j and the output class. The relevant equation and details are provided elaborately in the work of the authors [39].…”

Section: Binary Hybrid Ant Colony Optimization Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection

Ovhal

Kulkarni

Valadi³

2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Binary Hybrid Ant Colony Optimization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection

Ovhal

Kulkarni

Valadi³

2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection

Bhosale

Ovhal

Sane

et al. 2022

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Koti,

B.A

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.

show abstract

An Improved Ant Colony Optimization with Correlation and Gini Importance for Feature Selection

Cited by 4 publications

References 14 publications

Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection

Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection

Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection

Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Contact Info

Product

Resources

About