Hybrid information gain based fuzzy roughset feature selection in cancer microarray data

Chinnaswamy, Arunkumar; Ramakrishnan, S.

doi:10.1109/ipact.2017.8244875

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…Typically, a higher gain value corresponds to a lower entropy. IG has been used extensively in several applications for the diagnosis of cancerous diseases, such as in filtering informative genes for precise cancer classification [201], selecting breast cancer treatment factors based on the entropy formula [202], analyzing and classifying medical data of breast cancer [203], reducing the dimensionality of genes in multiclass cancer microarray gene expression datasets [204], and filtering irrelevant and redundant genes of cancer [201]. In [205], IG is utilized as a feature selection technique to eliminate redundant and irrelevant symptoms in datasets related to diabetes, breast cancer, and heart disease.…”

Section: Feature Selection Methods (Fs)mentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years, offering advanced tools and methodologies that promise to revolutionize patient outcomes. This review provides an exhaustive overview of the contemporary frameworks employed in the field, focusing on the objective of AI-driven analysis and dissecting methodologies across supervised, unsupervised, and ensemble learning. Specifically, we delve into techniques such as deep learning, artificial neural networks, traditional classification, and probabilistic models (PMs) under supervised learning. With its prowess in clustering and dimensionality reduction, unsupervised learning (USL) is explored alongside ensemble methods, including bagging and potent boosting algorithms. The thyroid cancer datasets (TCDs) are integral to our discussion, shedding light on vital features and elucidating feature selection and extraction techniques critical for AI-driven diagnostic systems. We lay out the standard assessment criteria across classification, regression, statistical, computer vision, and ranking metrics, punctuating the discourse with a real-world example of thyroid cancer detection using AI. Additionally, this study culminates in a critical analysis, elucidating current limitations and delineating the path forward by highlighting open challenges and prospective research avenues. Through this comprehensive exploration, we aim to offer readers a panoramic view of AI’s transformative role in thyroid cancer diagnosis, underscoring its potential and pointing toward an optimistic future.

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Section: Feature Selection Methods (Fs)mentioning

confidence: 99%

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Habchi,

Himeur,

Kheddar

et al. 2023

Systems

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“…H(Y) � − 􏽘 y∈Y p(y)log 2 (p(y)), (2) where x ∈ X, y ∈ Y, and p(x), p(y) represent the marginal probability of x and y, respectively. e conditional entropy and information gain [6][7][8] of X versus Y can be calculated in terms of the first two following formulas, respectively. e information gain ratio of X versus Y is the ratio of the information gain to the information entropy, which is formulated in the last following formula.…”

Section: Information Gain Ratiomentioning

confidence: 99%

“…Step (8). Generate a random r. If r is less than or equal to the mutation probability p m , then randomly select one chromosome from P 3 to perform the mutation operator described in Section 5.3. e final population is marked as P 4 .…”

Section: Detailed Steps Of Igrdcgamentioning

confidence: 99%

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

Feng

Niu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Single-cell RNA sequencing (scRNA-seq) is emerging as a promising technology. There exist a huge number of genes in a scRNA-seq data. However, some genes are high quality genes, and some are noises and irrelevant genes because of unspecific technology reasons. These noises and irrelevant genes may have a strong influence on downstream data analyses, such as a cell classification, gene function analysis, and cancer biomarker detection. Therefore, it is very significant to obviate these irrelevant genes and choose high quality genes by gene selection methods. In this study, a novel gene selection and classification method is presented by combining the information gain ratio and the genetic algorithm with dynamic crossover (abbreviated as IGRDCGA). The information gain ratio (IGR) is employed to eliminate irrelevant genes roughly and obtain a preliminary gene subset, and then the genetic algorithm with a dynamic crossover (DCGA) is utilized to choose high quality genes finely from the preliminary gene subset. The main difference between the IGRDCGA and the existing methods is that the DCGA and IGR are integrated first and used to select genes from scRNA-seq data. We conduct the IGRDCGA and several competing methods on some real-world scRNA-seq datasets. The obtained results demonstrate that the IGRDCGA can choose high quality genes effectively and efficiently and outperforms the other several competing methods in terms of both the dimensionality reduction and the classification accuracy.

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“…SU with harmony search algorithm is proposed for microarray data classification in Shreem et al (2016). IG with fuzzy rough sets and GA is proposed for cancer microarray data classification in Chinnaswamy and Srinivasan (2017) whereas IG with Binary Differential Evolution (BDE) is proposed for microarray data in Apolloni et al (2016). A hybrid approach that uses CFS filtering with iterative binary PSO is proposed in Jain et al (2018).…”

Section: Related Workmentioning

confidence: 99%

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Nagarajan

Babu

2021

Netw Model Anal Health Inform Bioinforma

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Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.

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Hybrid information gain based fuzzy roughset feature selection in cancer microarray data

Cited by 10 publications

References 23 publications

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

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