Analytical and Experimental Study of Filter Feature Selection Algorithms for High-dimensional Datasets

Pino, A.; Morell, Carlos

doi:10.2991/.2013.42

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The optimal solution is to use the dimension ACI reduction method as a data preprocessing step in reducing the complication and eliminating the redundant and irrelevant features in high-dimensional data. According to Pino and Morell [7], feature selection has been an ever-evolving problem due to the rise of big data in recent years. Feature selection aims to find the smaller number of essential features out of the highdimensional data, containing the best subset features with the least number of dimensions to improve the classification accuracy [8].…”

Section: Literature Review 21 Dimension Reductionmentioning

confidence: 99%

Improving handwritten digit recognition using hybrid feature selection algorithm

Chin

Lem

Wong

2022

ACI

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PurposeThe amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency.Design/methodology/approachThe minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features.FindingsThe hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR + support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR.Originality/valueIn view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.

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Section: Literature Review 21 Dimension Reductionmentioning

confidence: 99%

Improving handwritten digit recognition using hybrid feature selection algorithm

Chin

Lem

Wong

2022

ACI

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“…It is one of the standard methods for feature selection. The purpose of these techniques is to discard irrelevant or Information gain feature selection, entropy value has been calculated for whole data [12]. It is a supervised, univariate, simple, powerful, symmetrical and entropy-based feature selection algorithm.…”

Section: Information Gain Feature Selectionmentioning

confidence: 99%

Chi-Square MapReduce Model for Agricultural Data

Rȧjeswari

Suthendran

2018

JCSM

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Nowadays, agriculture plays a very significant role in economic growth. Decision making, crop selection and crop yield are the important issues in agriculture productions. Agricultural automation has lead to an incredible growth of software and applications to access the information. Agriculture database contains the farmer's details, land details, soil nutrient details, water levels details and etc. When the data set contains irrelevant, redundant and noisy data then it degrades the performance of the classifier model. The feature selection algorithm is used to improve the performance by selecting the relevant attributes and removing the irrelevant attributes from the database. In this paper, a novel idea is proposed by deploying chi-square technique in MapReduce model to handle large amount of agricultural data. The experimental results show that the proposed Chi-Square MapReduce model has high accuracy and less processing time than the existing feature selection methods.

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WITHDRAWN: Improving classification of protein binders for virtual drug screening by novel swarm-based feature selection techniques

Li¹,

Fong²,

Si³

et al. 2016

Computerized Medical Imaging and Graphics

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Analytical and Experimental Study of Filter Feature Selection Algorithms for High-dimensional Datasets

Cited by 3 publications

References 29 publications

Improving handwritten digit recognition using hybrid feature selection algorithm

Improving handwritten digit recognition using hybrid feature selection algorithm

Chi-Square MapReduce Model for Agricultural Data

WITHDRAWN: Improving classification of protein binders for virtual drug screening by novel swarm-based feature selection techniques

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