Novel machine learning approach for classification of high-dimensional microarray data

Musheer, Rabia; Verma, Chandan Kumar; Srivastava, Namita

doi:10.1007/s00500-019-03879-7

Cited by 61 publications

(30 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Independent component analysis (ICA) also has been used for the feature extraction of microarray gene expression data in numerous works [61]. For microarray data, Musheer et al proposed a novel (artificial bee colony) ABC-based feature selection approach [62], including two stages: ICAbased extraction method and ABC-based wrapper approach, respectively. Therefore, the merit of ICA is that the number of extracted features is always equal to the number of samples.…”

Section: Independent Component Analysismentioning

confidence: 99%

Feature dimensionality reduction: a review

Jia

Sun

Lian

et al. 2022

Complex Intell. Syst.

259

View full text Add to dashboard Cite

As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase the cost of data storage and computing; it also influences the efficiency and accuracy of dealing with problems. Feature dimensionality reduction as a key link in the process of pattern recognition has become one hot and difficulty spot in the field of pattern recognition, machine learning and data mining. It is one of the most challenging research fields, which has been favored by most of the scholars’ attention. How to implement “low loss” in the process of feature dimension reduction, keep the nature of the original data, find out the best mapping and get the optimal low dimensional data are the keys aims of the research. In this paper, two-dimensionality reduction methods, feature selection and feature extraction, are introduced; the current mainstream dimensionality reduction algorithms are analyzed, including the method for small sample and method based on deep learning. For each algorithm, examples of their application are given and the advantages and disadvantages of these methods are evaluated.

show abstract

Section: Independent Component Analysismentioning

confidence: 99%

Feature dimensionality reduction: a review

Jia

Sun

Lian

et al. 2022

Complex Intell. Syst.

259

View full text Add to dashboard Cite

show abstract

“…At present, many scholars have applied the machine learning method to cancer classification and thus designed various classification models and achieved satisfactory results. For example, Musheer et al [ 25 ] used a naive Bayes classifier to classify and evaluate six microarray cancer datasets after feature reduction, which proved that the algorithm has certain significance. Ye et al [ 1 ] applied the KNN classifier to evaluate the extracted information gene subset, which improved the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

show abstract

“…The selection of points as support vectors was influenced by the shape and character or condition of the features of the data. By getting the best features, the margin on the support vector can be maximized [23][24][25]. Equation (7) presents the function that must be optimized from the hyperplane margin where w is the unit vector found in the hyperplane.…”

Section: Figure 3 Cfs Flowchartmentioning

confidence: 99%

Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification

Hakim

Adiwijaya

Astuti

2021

IJECE

View full text Add to dashboard Cite

Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. Therefore, before performing classification, a feature selection technique must be used to eliminate features that do not have important information to support the classification process. The feature selection techniques used were ReliefF and correlation-based feature selection (CFS) and a classification technique used in this study is support vector machine (SVM). Several testing schemes were applied in this analysis to compare the performance of ReliefF and CFS with SVM. It showed that the ReliefF outperformed compared with CFS as microarray data classification approach.

show abstract

Novel machine learning approach for classification of high-dimensional microarray data

Cited by 61 publications

References 59 publications

Feature dimensionality reduction: a review

Feature dimensionality reduction: a review

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification

Contact Info

Product

Resources

About