Online Streaming Feature Selection via Conditional Independence

You, Dianlong; Wu, Xindong; Shen, Limin; He, Yi; Yuan, Xu; Chen, Zhe; Deng, Song; Ma, Chuan

doi:10.3390/app8122548

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…Definition 5 (Redundant [24]). A feature X i is redundant to the target variable T, if and only if it is weakly relevant to target variable T and has a Markov blanket, MB(X i ), then it is a subset of the Markov blanket of MB T .…”

Section: Preliminariesmentioning

confidence: 99%

Online Streaming Features Selection via Markov Blanket

et al. 2022

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Streaming feature selection has always been an excellent method for selecting the relevant subset of features from high-dimensional data and overcoming learning complexity. However, little attention is paid to online feature selection through the Markov Blanket (MB). Several studies based on traditional MB learning presented low prediction accuracy and used fewer datasets as the number of conditional independence tests is high and consumes more time. This paper presents a novel algorithm called Online Feature Selection Via Markov Blanket (OFSVMB) based on a statistical conditional independence test offering high accuracy and less computation time. It reduces the number of conditional independence tests and incorporates the online relevance and redundant analysis to check the relevancy between the upcoming feature and target variable T, discard the redundant features from Parents-Child (PC) and Spouses (SP) online, and find PC and SP simultaneously. The performance OFSVMB is compared with traditional MB learning algorithms including IAMB, STMB, HITON-MB, BAMB, and EEMB, and Streaming feature selection algorithms including OSFS, Alpha-investing, and SAOLA on 9 benchmark Bayesian Network (BN) datasets and 14 real-world datasets. For the performance evaluation, F1, precision, and recall measures are used with a significant level of 0.01 and 0.05 on benchmark BN and real-world datasets, including 12 classifiers keeping a significant level of 0.01. On benchmark BN datasets with 500 and 5000 sample sizes, OFSVMB achieved significant accuracy than IAMB, STMB, HITON-MB, BAMB, and EEMB in terms of F1, precision, recall, and running faster. It finds more accurate MB regardless of the size of the features set. In contrast, OFSVMB offers substantial improvements based on mean prediction accuracy regarding 12 classifiers with small and large sample sizes on real-world datasets than OSFS, Alpha-investing, and SAOLA but slower than OSFS, Alpha-investing, and SAOLA because these algorithms only find the PC set but not SP. Furthermore, the sensitivity analysis shows that OFSVMB is more accurate in selecting the optimal features.

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Section: Preliminariesmentioning

confidence: 99%

Online Streaming Features Selection via Markov Blanket

et al. 2022

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“…Also, selecting videos with high definition will affect the streaming quality. Get online when you have stable internet speeds due to efficient online streaming everything has to be related [26].…”

Section: Online Streaming Ecosystem 1) Definition Of a Online Streaming Ecosystemmentioning

confidence: 99%

Flipped Classroom with Challenge-Based Learning Model on an Online Streaming Ecosystem to Develop Coping Skills in Cyberbullying

Pornpongtechavanich¹,

Eumbunnapong²,

Piriyasurawong³

2021

IJIET

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The purposes of this research were: 1) to synthesize documents and international research on designing flipped classroom with challenge-based learning model on an online streaming ecosystem to develop coping skills in cyberbullying; 2) to design a flipped classroom with challenge-based learning model on an online streaming ecosystem to develop coping skills in cyberbullying; (3) to assess the suitability of the flipped classroom with challenge-based learning model on an online streaming ecosystem to develop coping skills in cyberbullying. Ten experts were selected using a specific selection: they had more than five years of relevant experience in the field with expertise in educational technology and information technology. The assessment ensures the suitability of the flipped classroom model with eco-challenging, for online learning to improve cyber bullying coping skills. The results showed that 41 international papers and research studies were synthesized, which concluded that there were eight factors that contribute to cyberbullying. The factors used for online streaming consisted of eight elements. The cyberbullying skills development process used a flipped classroom model and challenging learning to form a new learning model. When the components were completed, they were designed and evaluated. It was found that the flipped classroom with online learning ecosystem improves cyber bullying skills. It is highly suitable for developing cyberbullying prevention skills.

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“…Online feature selection for streaming features is an important branch of feature selection [4,8,21] that focuses on scenarios where the number of features changes over time, which must be processed in real-time and not after all the features arrive. Representative algorithms of this field include Grafting, Alpha-investing, OSFS, OGFS, and SAOLA.…”

Section: Related Workmentioning

confidence: 99%

“…Dimensionality reduction can combat the curse of dimensionality [1][2][3][4]. Feature selection chooses an "optimal feature subset" by following a certain criterion while removing redundant and irrelevant features of the class attribute during classification from the original high-dimensional feature set [2,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy†

Wang¹,

You²

2020

IJCIS

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The goals of feature selection are to remove redundant and irrelevant features from high-dimensional data, extract the "optimal feature subset" of the original feature space to improve the classification accuracy, and reduce the time complexity. Traditional feature selection algorithms are based on static feature spaces that are difficult to apply in dynamic streaming data environments. Existing works, such as Alpha-investing and Online Streaming Feature Selection (OSFS), and Scalable and Accurate OnLine Approach (SAOLA), have been proposed to serve the feature selection with streaming feature, but they have drawbacks, including low prediction accuracy and a large number of selected features if the streaming features exhibit characteristics such as low redundancy and high relevance. To address the limitations of the abovementioned works, we propose the algorithm of Online Streaming Feature Selection via Conditional dependence and Mutual information (OSFSCM) for streaming feature, which is found to be superior to Alpha-investing and OSFS for datasets with low redundancy and high relevance. The efficiency of the proposed OSFSCM algorithm is validated through a performance test on widely used datasets, e.g., NIPS 2003 and Causality Workbench. Through extensive experimental results, we demonstrate that OSFSCM significantly improves the prediction accuracy and requires fewer selected features compared with Alpha-investing and OSFS.

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Online Streaming Feature Selection via Conditional Independence

Cited by 8 publications

References 32 publications

Online Streaming Features Selection via Markov Blanket

Online Streaming Features Selection via Markov Blanket

Flipped Classroom with Challenge-Based Learning Model on an Online Streaming Ecosystem to Develop Coping Skills in Cyberbullying

Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy†

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