A Parallel Multilevel Feature Selection algorithm for improved cancer classification

Venkataramana, Lokeswari; Jacob, Shomona Gracia; Rajavel, R.

doi:10.1016/j.jpdc.2019.12.015

Cited by 15 publications

(10 citation statements)

References 32 publications

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“…The parallelization method designed in this paper mainly relies on Spark's unique data format--Resilient Distributed Datasets (RDD). In traditional parallel methods based on data parallelism, the training data is created as RDD [50]- [52]. In the parallelization method based on parametric parallelism proposed in this paper, the initial population of GA is created as RDD.…”

Section: Input Matrix Lstm Layermentioning

confidence: 99%

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

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Section: Input Matrix Lstm Layermentioning

confidence: 99%

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Lin

Wan

et al. 2020

IEEE Access

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“…At present, the application of machine learning methods to cancer classification is a significant research field in bioinformatics [4,5]. Many traditional machine learning methods have been successfully applied to the classification analysis of gene expression data [6][7][8][9], such as RF, decision tree, KNN, and neural networks. However, with the increasing amounts and diversification of data, the traditional classification algorithm has been unable to meet the requirements of processing existing data and solving practical problems [10,11].…”

Section: Introductionmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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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.

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“…Therefore, an appropriate method should be selected to eliminate abnormal samples. IF is a famous unsupervised abnormally detection algorithm, which divides the sample space by feature points until all sample points are isolated 11 . In the IF model, normal samples with large similarity with surrounding samples need to be divided several times to be isolated, and abnormal samples with large differences from surrounding samples are easily isolated.…”

Section: Methodsmentioning

confidence: 99%

“…IF is a famous unsupervised abnormally detection algorithm, which divides the sample space by feature points until all sample points are isolated. 11 In the IF model, normal samples with large similarity with surrounding samples need to be divided several times to be isolated, and abnormal samples with large differences from surrounding samples are easily isolated. In addition, the value of decision function is used to represent the abnormal situation of samples in the IF model.…”

Section: Abnormal Detection and Sample Divisionmentioning

confidence: 99%

Detection of talcum powder content in wheat flour by near infrared spectroscopy based on multilevel feature selection

Sun

Bai

et al. 2022

Journal of Chemometrics

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In this study, the near infrared spectroscopy (NIRS) technology was used to quantitatively detect talcum powder in 82 wheat flour samples. Based on anomaly detection, sample division, and spectral preprocessing, four models were established to predict the content of talcum powder in wheat flour. Among them, the performance of Bayesian ridge regression (BRR) combined with second derivative (2D) was proved to be the best. In addition, 46 effective features were selected using a multilevel feature method combining improved particle swarm optimization (PSO) and genetic algorithm (GA). At this time, the coefficient of determination (R2_PRE), root mean square error of prediction (RMSEP), and relative percent difference (RPD) values of the BRR model on the prediction set reached 0.9802, 0.8914, and 6.9263. The results showed that NIRS technology was feasible in detecting the content of talcum powder in wheat flour. At the same time, the effectiveness of multilevel method was better than that of single‐level method, and the performance of improved POS was better than that of PSO.

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A Parallel Multilevel Feature Selection algorithm for improved cancer classification

Cited by 15 publications

References 32 publications

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Detection of talcum powder content in wheat flour by near infrared spectroscopy based on multilevel feature selection

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