Causality-based Feature Selection

Yu, Ke; Guo, Xianjie; Liu, Lin; Li, Jiuyong; Wang, Hao; Ling, Zhaolong; Wu, Xindong

doi:10.1145/3409382

Cited by 125 publications

(72 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1 displays the results for the three test cases (TC) used in Yu et al (2018). The standard metrics (Aliferis et al (2010b); Yu et al (2018); Yu et al (2019)) were applied:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fast Markov Blanket Discovery Without Causal Sufficiency

Jeronymo

Maciel

2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

Faster feature selection algorithms become a necessity as Big Data dictates the zeitgeist. An important class of feature selectors are Markov Blanket (MB) learning algorithms. They are Causal Discovery algorithms that learn the local causal structure of a target variable. A common assumption in their theoretical basis, yet often violated in practice, is causal sufficiency: the requirement that all common causes of the measured variables in the dataset are also in the dataset. Recently, Yu et al. (2018) proposed the M3B algorithm, the first to directly learn the MB without demanding causal sufficiency. The main drawback of M3B is that it is time inefficient, being intractable for high-dimensional inputs. In this paper, we derive the Fast Markov Blanket Discovery Algorithm (FMMB). Empirical results that compare FMMB to M3B on the structural learning task show that FMMB outperforms M3B in terms of time efficiency while preserving structural accuracy. Five real-world datasets where used to contrast both algorithms as feature selectors. Applying NB and SVM classifiers, FMMB achieved a competitive outcome. This method mitigates the curse of dimensionality and inspires the development of local-toglobal algorithms.

show abstract

“…1 displays the results for the three test cases (TC) used in Yu et al (2018). The standard metrics (Aliferis et al (2010b); Yu et al (2018); Yu et al (2019)) were applied:…”

Section: Methodsmentioning

confidence: 99%

“…It combines the strategies of simultaneous and divide-and-conquer algorithms to derive a method capable of simultaneously learning the MB while distinguishing PC from spouses. Yu et al (2019) is an extensive review on DAG MB learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fast Markov Blanket Discovery Without Causal Sufficiency

Jeronymo

Maciel

2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

show abstract

“…The methods by selecting and transforming multi-dimensional features into low-dimensional features have been proposed to improve the quality of features, reduce computational complexity, and improve recognition accuracy. [92][93][94][95][96][97][98] In present machine learning feature engineering, these two powerful technologies have shown great potential in various applications, such as the text data, [99] planetary system metrics, [100] clinical medicine, [101] human hand motion classification, [102] human activity recognition, [103] etc. In materials science, the transformation in various features is also important, which might directly affect the analytical accuracy in structure and properties.…”

Section: Strengthening the Analysis And Optimization On Featuresmentioning

confidence: 99%

Innovative Materials Science via Machine Learning

Gao

Min

Fang

et al. 2021

Adv Funct Materials

101

View full text Add to dashboard Cite

Nowadays, the research on materials science is rapidly entering a phase of data-driven age. Machine learning, one of the most powerful data-driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future.

show abstract

“…In traditional MB discovery techniques, features must be present before learning begins. Different algorithms developed for MB learning, which is based on traditional concepts such as Incremental Association-Based Markov Blanket (IAMB) [16], Max-Min Markov Blanket (MMMB) [17], HITON-MB (HITON-MB) [18], Simultaneous Markov Blanket (STMB) [19], Iterative Parent-Child-based MB (IPCMB) [19], Balanced Markov Blanket (BAMB) [6], and Efficient and Effective Markov Blanket (EEMB) [7]. While MB is learning the Parents-Child and Spouses of the target feature, T cannot differentiate by the IAMB [16] algorithm.…”

Section: Related Workmentioning

confidence: 99%

Online Streaming Features Selection via Markov Blanket

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Causality-based Feature Selection

Cited by 125 publications

References 52 publications

Fast Markov Blanket Discovery Without Causal Sufficiency

Fast Markov Blanket Discovery Without Causal Sufficiency

Innovative Materials Science via Machine Learning

Online Streaming Features Selection via Markov Blanket

Contact Info

Product

Resources

About