A New Dimensional Reduction Based on Cuttlefish Algorithm for Human Cancer Gene Expression

Arshak, Yousif; Eesa, Adel Sabry

doi:10.1109/icoase.2018.8548908

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Arshak and Eesa [65] proposed a feature selection method for dimensionality reduction based on the cuttlefish algorithm (CFA) for gene classification. The cuttlefish was used to generate a subset of the optimal features.…”

Section: A Feature Selectuion Methodsmentioning

confidence: 99%

“…Whereas, in research [51] the execution time was better, about 50% to 70% when compared with IG and ReliefF techniques. Moreover, in the researches [65] and [70], execution time was reduced by 40% and 50% by the CFA and GA. However, the best computational time was achieved when the Ant system integrated with the clustering algorithm in the research [58] where it was 5-8 times better compared to the other three ACO algorithms.…”

Section: Sellami and Farahmentioning

confidence: 96%

“…Also, about 50% of the reviewed feature selection methods were based on the optimization approaches. Where these methods used several optimized algorithms such as PSO in [53] and [69], EA in [52], Ant in [58], GA in [64] and [70], CAF in [65] and [68] and deep learning in [61] and [65]. In [53] and [69] PSO algorithm was used to incorporate the features information into search space and hence selecting the most desired features and removing not required ones.…”

Section: Sellami and Farahmentioning

confidence: 99%

“…According to the research [64] the average of reduction was 93%, and in [70] used feature dataset was reduced by 50%. Also, the CFA has a great role in reducing the dimensionality and computational time in [65] the dataset was reduced up to 93% and in [68] the time was less than compared to other optimization algorithms the authors did a comparison with. The deep neural network was used in order to improve classification accuracy.…”

Section: Sellami and Farahmentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

548

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: A Feature Selectuion Methodsmentioning

confidence: 99%

Section: Sellami and Farahmentioning

confidence: 96%

Section: Sellami and Farahmentioning

confidence: 99%

Section: Sellami and Farahmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

548

210

View full text Add to dashboard Cite

show abstract

“…Cuttlefish algorithm (CFA) is a new bio-inspired optimization method, which was proposed in 2013 by (Eesa, 2013) and was successfully used as a possible alternative tool for global optimization problems (Eesa, 2014), dimensionality reduction (Arshak & Eesa, 2018) and clustering problems (Eesa & Orman, 2019). This algorithm simulates the process of light reflection through the three skin layers of a cuttlefish, including iridophores, chromatophores, and leucophores.…”

Section: Cuttlefish Optimization Algorithm (Cfa)mentioning

confidence: 99%

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

Eesa¹,

sadiq²,

Hassan³

et al. 2021

Uludağ University Journal of the Faculty of Engineering

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Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are increasingly in demand. Consequently, numerous illicit activities by external and internal attackers need to be detected. Thus, earlier detection of such activities is necessary for protecting data and information. In this paper, we investigated the use of the Cuttlefish optimization algorithm as a new rule generation method for the classification task to deal with the intrusion detection problem. The effectiveness of the proposed method was tested using KDD Cup 99 dataset based on different evaluation methods. The obtained results were also compared with the results obtained by some classical well-known algorithms namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighborhood (K-NN). Our experimental results showed that the proposed method demonstrates a good classification performance and provides significantly preferable results when compared with the performance of other traditional algorithms. The proposed method produced 93.9%, 92.2%, and 94.7% in terms of precision, recall, and area under curve, respectively.

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A Review Based on Machine Learning for Feature Selection and Feature Extraction

Lakshme

Kumar

2022

Advancements in Smart Computing and Information Security

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A New Dimensional Reduction Based on Cuttlefish Algorithm for Human Cancer Gene Expression

Cited by 7 publications

References 11 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

A Review Based on Machine Learning for Feature Selection and Feature Extraction

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