An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm

Mohsin, Mohamad Farhan Mohamad; Hamdan, Abdul Razak; Bakar, Azuraliza Abu

doi:10.1109/icitcs.2014.7021732

Cited by 4 publications

(2 citation statements)

References 9 publications

(10 reference statements)

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“…In this work, two experiments are performed to study the feasibility and superiority of the proposed approach. In the first experiment, GGA-DCA and state-of-the-art DCA expansion algorithms (NIDDCA [1], FLA-DCA [17], GA-PSM [29], and the SVM-DCA [20]) perform classification tasks on the 24 data sets. For the purpose of baseline comparison, the well-known classifiers, the KNN, the DT, the XGboost, the RF, and the ERT, also perform classification tasks on the 24 data sets.…”

Section: Experiments Setupmentioning

confidence: 99%

“…However, it is essential to realize that relevance/importance according to those definitions does not imply membership in the optimal feature subset, and irrelevance does not mean that a feature can not be in the optimal feature subset [18]. In addition, machine learning algorithms, such as K-Nearest Neighbors (KNN) [19] and Support Vector Machine (SVM) [20], are employed for input signal generation. Moreover, Zhou et al [1] utilized numerical differential to extract features based on the data changes of the selected features.…”

Section: Introductionmentioning

confidence: 99%

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Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Zhang¹,

Liang²,

Dong³

2023

Computer Modeling in Engineering &Amp; Sciences

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The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data preprocessing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to remove unimportant attributes. Ignoring filtered features and applying expertise may not produce an optimal classification result. To overcome these limitations, this study models feature selection and signal categorization into feature grouping problems. This study hybridizes Grouping Genetic Algorithm (GGA) with DCA to propose a novel DCA version, GGA-DCA, for accomplishing feature selection and signal categorization in a search process. The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically. In this study, the data coding and operators of GGA are redefined for grouping tasks. The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

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Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Zhang¹,

Liang²,

Dong³

2023

Computer Modeling in Engineering &Amp; Sciences

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A Fibrosis Diagnosis Clinical Decision Support System Using Fuzzy Knowledge

et al. 2018

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Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification

Şahin

Di̇ri̇

2021

Balkan Journal of Electrical and Computer Engineering

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In the field of biomedicine, applications for the identification of biomarkers require a robust gene selection mechanism. To identify the characteristic marker of an observed event, the selection of attributes becomes important. The robustness of gene selection methods affects the detection of biologically meaningful genes in tumor diagnosis. For mapping, a sequential feature long short-term memory (LSTM) network was used with artificial immune recognition systems (AIRS) to remember gene sequences and effectively recall learned sequential patterns. An attempt was made to improve AIRS with LSTM, which is a type of RNNs, to produce discriminative gene subsets for finding biologically meaningful genes in tumor diagnosis. The algorithms were evaluated using six common cancer microarray datasets. By converging to the intrinsic information of the microarray datasets, specific groups such as functions of the coregulated groups were observed. The results showed that the LSTM-based AIRS model could successfully identify biologically significant genes from the microarray datasets. Furthermore, the predictive genes for biological sequences are important in gene expression microarrays. This study confirmed that different genes could be found in the same pathways. It was also found that the gene subsets selected by the algorithms were involved in important biological pathways. In this manuscript, we tried an LSTM network on our learning problem. We suspected that recurrent neural networks would be a good architecture for making predictions. The results showed that the optimal gene subsets were based on the suggested framework, so they should have rich biomedical interpretability.

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An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm

Cited by 4 publications

References 9 publications

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

A Fibrosis Diagnosis Clinical Decision Support System Using Fuzzy Knowledge

Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification

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