Novel and efficient randomized algorithms for feature selection

Wang, Zigeng; Xiao, Xia; Rajasekaran, Sanguthevar

doi:10.26599/bdma.2020.9020005

Cited by 28 publications

(14 citation statements)

References 44 publications

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“…The filtering feature selection method has the advantages of strong independence, fast running speed and low computational complexity in machine learning algorithms, but makes it difficult to completely delete redundant features when there are many redundant features and high target relevance 41 . Wrapping methods can be independent of machine learning models but typically have high computational costs 42 . The embedded method embeds feature selection into other algorithms and selects new features during the training process, which can effectively improve the efficiency of model learning 43 .…”

Section: Related Workmentioning

confidence: 99%

Predicting students’ performance in e-learning using learning process and behaviour data

Qiu

Zhang

Sheng

et al. 2022

Sci Rep

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E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.

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Section: Related Workmentioning

confidence: 99%

Predicting students’ performance in e-learning using learning process and behaviour data

Qiu

Zhang

Sheng

et al. 2022

Sci Rep

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“…Filter-based methods select features based on their score computed by different statistical approaches. Wrapper-based FS methods select features by iteratively generating and calculating the predictive performance of the features subset [12]. Hybrid or embedded feature selection combines the merits of filter and wrapping methods.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

Siddiqi

Pak

2020

Electronics

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In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems (IDSs) are considered as one of the most suitable solutions for big data security. Despite the progress in ML, unrelated features can drastically influence the performance of an IDS. Feature selection plays a significant role in improving ML-based IDSs. However, the recent growth of dimensionality in data poses quite a challenge for current feature selection and extraction methods. Due to high data dimensionality, feature selection methods suffer in terms of efficiency and effectiveness. In this paper, we are introducing a new process flow for filter-based feature selection with the help of a transformation technique. Generally, normalization or transformation is implemented before classification. In our proposed model, we implemented and evaluated the effects of normalization before feature selection. To present a clear analysis on the effects of power transformation, five different transformations were implemented and evaluated. Furthermore, we implemented and compared different feature selection methods with the proposed process flow. Results show that compared with existing process flow and feature selection methods, our proposed process flow for feature selection can locate a more relevant set of features with high efficiency and accuracy.

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“…Recently, with the adverse growth of big data in all science and engineering domains especially in physical, biological, and biomedical sciences, there is faster development of networking, data collection, and data storage capacity. [1][2][3][4][5][6][7][8][9][10][11] Data mining refers to be the activity of searching relevant or pertinent information by processing the big datasets. This has greatly influenced in the decision-making process.…”

Section: Introductionmentioning

confidence: 99%

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

Selvi¹,

Valarmathi²,

Devadas

2022

Concurrency and Computation

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Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.

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Novel and efficient randomized algorithms for feature selection

Cited by 28 publications

References 44 publications

Predicting students’ performance in e-learning using learning process and behaviour data

Predicting students’ performance in e-learning using learning process and behaviour data

Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

Improved meta‐heuristic algorithm for selecting optimal features: A big data classification model

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