Feature Selection for Classification using Principal Component Analysis and Information Gain

Omuya, Erick Odhiambo; Okeyo, George; Kimwele, Michael

doi:10.1016/j.eswa.2021.114765

Cited by 174 publications

(60 citation statements)

References 16 publications

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“…GDP, employment, and FDI as economic and energy intensity as an environmental factor have been taken in the study following Li et al (2021). To obtain the index for sustainable development, we use principal component analysis (PCA) (Mahmoudi et al 2021;Odhiambo et al 2021;Schreiber 2021). The variable description is given in Table 1, and the results of the principal component analysis are given in Table 2.…”

Section: Data and Variables Descriptionmentioning

confidence: 99%

Striving towards sustainable development: how environmental degradation and energy efficiency interact with health expenditures in SAARC countries

Zhong

Ren

Akbar

et al. 2022

Environ Sci Pollut Res

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The previous studies focused on environmental issues, identifying their root causes, urging prompt action to reduce environmental degradation. In this context, the current article extends the literature by incorporating the ecological impacts on the health sector and the role of sustainable development. The present study adds to the body of knowledge by examining the relationship between CO 2 emissions, sustainable development, energy efficiency, energy intensity, and health expenditures for SAARC countries from 2000 to 2020. Fully modified OLS (FMOLS) and dynamic OLS (DOLS) are used and diagnostic tests to check the association between the variables. The empirical analysis validated the long-run impact of the examined factors on health expenditures. The results show that energy efficiency and sustainable development have a statistically significant negative effect on health expenditures, vice-versa for CO 2 emission. Energy efficiency, energy intensity, and CO 2 emissions have been shown to have a one-way causative relationship with health expenditures, but sustainable development and economic growth have a two-way causation relationship. The better health status of the SAARC economies necessitates the establishment of long-term development strategies, environmental sustainability, and an examination of the energy sector. This work's conceptual and empirical advances have significant policy ramifications for this part of the globe and its efforts to improve sustainability.

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Section: Data and Variables Descriptionmentioning

confidence: 99%

Striving towards sustainable development: how environmental degradation and energy efficiency interact with health expenditures in SAARC countries

Zhong

Ren

Akbar

et al. 2022

Environ Sci Pollut Res

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“…The selection of important and suitable identification features can not only simplify the calculation but also allow understand the causal relationship, which is a critical part of machine learning. The advantages of feature selection [39][40][41] include (1) reduce data collection cost, (2) improved data processing through the removal of redundant data and simplification of patterns, resulting in faster computing, (3) improved data interpretation since feature selection improves prediction results, and accelerates model derivation and knowledge discovery. The objective is to find out the most relevant classification features, reduce the dimensions, and correct training samples so as to select important and effective conditional attributes.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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“…The major advantage of using PCA on the dataset are removal of the correlation between the components, expediating the algorithm performance, resolving the problem of overfitting in the model and better visualization. However, due to feature reduction, there are chances of information loss [65].…”

Section: Principal Component Analysismentioning

confidence: 99%

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods

Shokat

Riaz

Rizvi

et al. 2022

Sensors

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Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

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Feature Selection for Classification using Principal Component Analysis and Information Gain

Cited by 174 publications

References 16 publications

Striving towards sustainable development: how environmental degradation and energy efficiency interact with health expenditures in SAARC countries

Striving towards sustainable development: how environmental degradation and energy efficiency interact with health expenditures in SAARC countries

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods

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