An Efficient Algorithm for Feature Selection Problem in Gene Expression Data: A Spider Monkey Optimization Approach

Kancharla, Gangadhara Rao; Eluri, Nageswara Rao; Dara, Suresh; Ansari, Nishath

doi:10.2139/ssrn.3351002

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…En resumen, AGCM obtiene mejores resultados en comparación a AGA+MIMC, AGMIMC y AG. No logra superar a AMA aunque esto puede deberse al número de evaluaciones, las cuales no son reportadas en el trabajo [36]. Similar ocurre con ARKB+GI con la instancia leucemia aunque se observa que el AGCM lo supera ampliamente en colon.…”

Section: E Comparación Con Resultados De La Literaturaunclassified

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

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Olivera

Carballido

et al. 2020

IEEE Latin Am. Trans.

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Section: E Comparación Con Resultados De La Literaturaunclassified

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Rojas

Olivera

Carballido

et al. 2020

IEEE Latin Am. Trans.

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“…Single objective wrapper techniques generally serve the purpose by restricting the length of the feature set, or by enhancing the classification efficiency, or by aggregating these targets [41]. For a better understanding of the single-objective evolutionary methods for solving FS task the interested reader can refer to DA [21], SSA [26], [42], HS [43], TLBO [28], grasshopper optimization [44], Jaya algorithm [22], [45], HHO [18], atom search [46], SMO [24], SHO [25], CS [47], ALO [48], ABC [30], FOA [49], FPA [50], and WOA [51], [52] etc.…”

Section: Related Workmentioning

confidence: 99%

“…The genetic algorithm (GA) is utilized for the resolution of the FS issue in numerous research studies as a well-known metaheuristic method [13]- [16]. Moreover, other algorithms such as Grey Wolf Optimization (GWO) [17], Harris Hawk Optimizer (HHO) [18]- [20], Forest Optimizer (FO) [3], Dragonfly Algorithm (DA) [21], Jaya optimization method [22], Bacteria foraging method [23], spider monkey optimization [24], spotted hyena method [25], salp swarm algorithm [26], ant lion optimizer [27], and teaching-learning optimization [28] etc., have been employed to tackle FS tasks.…”

Section: Introductionmentioning

confidence: 99%

A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

et al. 2022

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Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.

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“…First, KDDCUP99, NSL-KDD, and CIDD datasets are pre-processed for choosing a subset of the features, followed by dimension reduction, and finally, they perform a normalization of the data. In [24], the authors propose a random neural network and an artificial bee colony algorithm (RNN-ABC), based on the intrusion detection system (IDS). A NSL-KDD data set is used for this model.…”

Section: Related Workmentioning

confidence: 99%

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

et al. 2020

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The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

show abstract

An Efficient Algorithm for Feature Selection Problem in Gene Expression Data: A Spider Monkey Optimization Approach

Cited by 5 publications

References 8 publications

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

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