Feature Selection of Micro-array expression data (FSM) - A Review

Lamba, Monika; Munjal, Geetika; Gigras, Yogita

doi:10.1016/j.procs.2018.05.127

Cited by 18 publications

(5 citation statements)

References 33 publications

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“…This dataset features real capture of network-traffic data [52]. Enabling machine learning algorithms to model is affected by attributes [53]. Therefore, this study used the entire dataset as the population of this study, thus not requiring external validity.…”

Section: Study Validitymentioning

confidence: 99%

Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

Zeinalpour

Ahmed

2022

Electronics

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The curse of dimensionality, due to lots of network-traffic attributes, has a negative impact on machine learning algorithms in detecting distributed denial of service (DDoS) attacks. This study investigated whether adding the filter and wrapper methods, preceded by combined clustering algorithms using the Vote classifier method, was effective in lowering the false-positive rates of DDoS-attack detection methods. We examined this process to address the curse of dimensionality of machine learning algorithms in detecting DDoS attacks. The results of this study, using ANOVA statistical analyses, showed that incorporating the wrapper method had superior performance in comparison with the filter and clustering methods. IT professionals aim at incorporating effective DDoS-attack detection methods to detect attacks. Therefore, the contribution of this study is that incorporating the wrapper method is the most suitable option for organizations to detect attacks as illustrated in this study. Subsequently, IT professionals could incorporate the DDoS-attack detection methods that, in this study, produced the lowest false-positive rate (0.012) in comparison with all the other mentioned studies.

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Section: Study Validitymentioning

confidence: 99%

Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

Zeinalpour

Ahmed

2022

Electronics

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“…Embedded methods combine the benefits of both Wrapper models and filter methods using taking different rating parameters in various steps of the search. This model includes feature selection during the training process without splitting the data into training, and testing sets [32,33]. This Embedded idea is similar to wrappers but less prone to overfitting and less computationally expensive.…”

Section: Feature Selectionmentioning

confidence: 99%

“…This Embedded idea is similar to wrappers but less prone to overfitting and less computationally expensive. Moreover, the significant restriction with this method is that it takes decisions based on the classifiers [17,33].…”

Section: Feature Selectionmentioning

confidence: 99%

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Credit Risk Assessment Using Learning Algorithms for Feature Selection

Hassani

Meybodi

Hajihashemi³

2020

Fuzzy Information and Engineering

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Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

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“…The efficient dimension reduction technique needs to be chosen to reduce the number of non-relevant features present in the dataset. Gene selection is also an important factor in removing essential elements which improve precision (Lamba et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Classification of Gene Expression Data Using Feature Selection Based on Type Combination Approach Model With Advanced Feature Selection Technology

Siddesh

Gururaj

2021

International Journal of Cognitive Informatics and Natural Intelligence

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A key step in addressing the classification issue was the selection of genes for removing redundant and irrelevant genes. The proposed Type Combination Approach –Feature Selection(TCA-FS) model uses the efficient feature selection methods, and the classification accuracy can be enhanced. The three classifiers such as K Nearest Neighbour(KNN), Support Vector Machine(SVM) and Random Forest(RF) are selected for evaluating the opted feature selection methods, and prediction accuracy. The effects of three new approaches for feature selection are Improved Recursive Feature Elimination (IRFE), Revised Maximum Information co-efficient (RMIC), as well as Upgraded Masked Painter (UMP), are analysed. These three proposed techniques are compared with existing techniques and are validated with (i) Stability determination test. (ii) Classification accuracy. (iii) Error rates of three proposed techniques are analysed. Due to the selection of proper threshold on classification, the proposed TCA-FS method provides a higher accuracy compared to the existing system.

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Feature Selection of Micro-array expression data (FSM) - A Review

Cited by 18 publications

References 33 publications

Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Classification of Gene Expression Data Using Feature Selection Based on Type Combination Approach Model With Advanced Feature Selection Technology

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