Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review

Chan, Jireh Yi-Le; Leow, Steven Mun Hong; Bea, Khean Thye; Cheng, Wai Khuen; Phoong, Seuk Wai; Hong, Zeng-Wei; Chen, Yen‐Lin

doi:10.3390/math10081283

Cited by 157 publications

(84 citation statements)

References 64 publications

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“…The presence of multicollinearity can be assessed for example by the variance inflation factor 104 or the weighted variance inflation factor, 105 and the problem can be minimized by combining correlated variables to a single variable or by employing regression algorithms that can deal with multicollinearity (e.g., Ridge regression and Lasso regression). 106 In addition, it is necessary to carefully design the GLM regression matrix given the SPA-fNIRS data to avoid underfitting and overfitting. Cross-validated Bayesian model selection is a solution for this issue.…”

Section: Systemic Physiology Augmented Functional Near-infrared Spect...mentioning

confidence: 99%

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

et al. 2022

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. In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future.

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Section: Systemic Physiology Augmented Functional Near-infrared Spect...mentioning

confidence: 99%

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

et al. 2022

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“…Although this creates opportunities to improve model performance, it also creates serious problems during the data analysis process. One of these problems is known as the multicollinearity problem [69]. Multicollinearity is a problem that arises when the input features of a dataset have a strong correlation with more than one of the other features in the dataset.…”

Section: Dimensional Reduction Using Lr-pcamentioning

confidence: 99%

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Alhussan

Ghoneim

Atteia

et al. 2022

Sensors

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One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.

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“…Financial time series usually come with a high degree of variability and extensive noise to disrupt the forecasting performance [11,12]. The study [13] claimed that the financial market might have a complex correlation across different markets, companies, or countries. Based on this, later studies attempted to address the forecasting problem by modelling the correlation between the variables of different market [14].…”

Section: Introductionmentioning

confidence: 99%

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

et al. 2022

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Stock forecasting is a significant and challenging task. The recent development of web technologies has transformed the communication channel to allow the public to share information over the web such as news, social media contents, etc., thus causing exponential growth of web data. The massively available information might be the key to revealing the financial market’s unexplained variability and facilitating forecasting accuracy. However, this information is usually in unstructured natural language and consists of different inherent meanings. Although a human can easily interpret the inherent messages, it is still complicated to manually process such a massive amount of textual data due to the constraint of time, ability, energy, etc. Due to the different properties of text sources, it is crucial to understand various text processing approaches to optimize forecasting performance. This study attempted to summarize and discuss the current text-based financial forecasting approaches in the aspect of semantic-based, sentiment-based, event-extraction-based, and hybrid approaches. Afterward, the study discussed the strength and weakness of each approach, followed with their comparison and suitable application scenarios. Moreover, this study also highlighted the future research direction in text-based stock forecasting, where the overall discussion is expected to provide insightful analysis for future reference.

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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review

Cited by 157 publications

References 64 publications

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

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