A genetic filter for cancer classification on gene expression data

Kim, Yong-Hyuk; Yoon, Yourim

doi:10.3233/bme-151503

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…In future studies, it is anticipated that QC and correction using machine learning will have further improved performance by understanding relationships with other data through methods such as a dimensional reduction technique [35][36][37]. Furthermore, this study may become the basis of leading to practical studies such as the valuation of collected data and prediction of sensor malfunction.…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Kim

Park²,

Choi³

et al. 2020

Computational Intelligence and Neuroscience

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We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Kim

Park²,

Choi³

et al. 2020

Computational Intelligence and Neuroscience

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“…Filter-based feature selection [31][32][33] has the advantage of deriving feature subsets by identifying correlations between features within a relatively short time; however, it has the disadvantage that it may be difficult to quantify relevance and redundancy between selected features. In this study, a new fitness function was devised to emphasize the advantages and make up for the disadvantages.…”

Section: Genetic Filtermentioning

confidence: 99%

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

2021

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Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

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“…Lastly, filtering method [17] is applied to select those genes that have p-value less than 0.05. This is because p value will determine the significant towards cancer mutation.…”

Section: Figure 3 Step 2 Normalizationmentioning

confidence: 99%

An Effective Pre-Processing Phase for Gene Expression Classification

Seah¹,

Kasim²,

Fudzee³

et al. 2018

IJEECS

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A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.

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A genetic filter for cancer classification on gene expression data

Cited by 8 publications

References 31 publications

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

An Effective Pre-Processing Phase for Gene Expression Classification

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