A comparative study of feature selection and feature extraction methods for financial distress identification

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Gu et al. conducted a comprehensive survey on the design and application of electrocatalysts powered by machine learning techniques [1]. They presented a novel approach that utilizes Artificial Neural Networks (ANN) in conjunction with the SHAP (SHapley Additive exPlanations) method to optimize membrane electrode assemblies. The ANN model demonstrated high accuracy in predicting key performance metrics, achieving root mean square error (RMSE) values of 43.536 mW cm−2 for power density and 0.070 gPt kW−1 for platinum utilization. Additionally, the SHAP method was employed to identify the most influential features affecting the target outputs, providing valuable insights into the optimization process [1]. However, this paper raises significant theoretical and empirical concerns regarding the use of ANN in conjunction with SHAP due to the model-specific nature of these techniques, which can lead to erroneous interpretations. It appears that Gu et al. may not fully grasp the fundamental principles underlying machine learning. In supervised machine learning models like ANN, two types of accuracy are crucial: target prediction accuracy and feature importance reliability. While target prediction accuracy can be validated against known ground truth values, the derived feature importances from models lack equivalent ground truth for validation. As a result, achieving high target prediction accuracy does not ensure that the feature importances are also reliable, since there are no established ground truth values for these features. The function call “explain = SHAP(model)” further indicates that SHAP may inherit and potentially amplify any biases present in the feature importances derived from the underlying model (ANN), leading to misleading interpretations of the results [2-5]. This highlights the importance of critically evaluating both the predictions and the interpretability provided by model-agnostic methods like SHAP. In light of these concerns, the paper advocates for a more robust and multifaceted approach utilizing unsupervised machine learning techniques, such as Feature Agglomeration (FA) and Highly Variable Gene Selection (HVGS). FA is a dimensionality reduction technique that aggregates similar features, thereby simplifying the data set and reducing noise, which can enhance the interpretability of the model and the reliability of its predictions. HVGS focuses on selecting a subset of features that exhibit significant variability across samples, ensuring that only the most informative features are retained for further analysis. Following the feature selection process, the authors suggest employing nonlinear nonparametric statistical methods, such as Spearman's correlation, to assess the relationships between features and outcomes. Spearman's correlation evaluates the strength and direction of the association between ranked variables, making it particularly useful in identifying monotonic relationships that do not necessarily follow a linear pattern. The accompanying p-values provide a measure of statistical significance, offering insights into the reliability of these correlations. By leveraging these advanced methods, researchers can achieve a deeper understanding of the key factors influencing electrocatalyst performance, while also mitigating the risks associated with relying solely on model-specific interpretations. Yoshiyasu Takefuji completed this study and wrote this article. The author has nothing to report. The author has nothing to report. The author declares no conflicts of interest. The author has nothing to report.

Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

Dimensionality Reduction is usually achieved on the feature space by adopting any one of the prescribed methods that fall under the selected technique. Feature selection and Feature extraction being the two aforesaid techniques of reducing dimensionality, the former discards certain features that may be useful at a later stage whereas the latter re-constructs its features into a simpler dimension thereby preserving all its initial characteristics. The sole purpose of this survey is to provide an adequate comprehension of the different dimensionality reduction techniques that exist currently and also to introduce the applicability of any one of the prescribed methods depending upon the given set of parameters and varying conditions as described, under each algorithm’s usage statistics. This paper also presents guidelines where in, selection of the best possible algorithm for a specific instance can be determined with ease when a condition arises where in two or more algorithms may be suitable for executing the aforementioned task.

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

A modified genetic algorithm and weighted principal component analysis based feature selection and extraction strategy in agriculture

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models.

Aim/Purpose: This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background: The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology: The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman’s (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution: This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings: Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations for Practitioners: Decision makers and top management are encouraged to focus on the identified highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendation for Researchers: This research can be considered a stepping stone to investigating the impact of COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society: Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research: Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results.

Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies

A Review of Dimensionality Reduction Techniques for Efficient Computation

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

High-dimensional systems are increasingly common in fields such as remote sensing, communications, and biomedical signal processing, where data typically comprises multivariate signals with interdependent components. These systems often suffer from noise contamination, which can significantly reduce the fidelity of signal interpretation. Traditional noise-reduction techniques, such as Wiener filtering or wavelet-based methods, frequently fail to perform optimally when applied to high-dimensional data due to challenges associated with high variance, noise correlation, and large computational demands [1-5]. In this article, we review state-of-the-art signal-processing algorithms for noise reduction specifically designed for high-dimensional systems. We focus on dimensionality reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), and kernel-based approaches, which have shown promise in improving signal fidelity while reducing noise [6, 7]. Furthermore, we introduce a novel hybrid algorithm that combines dimensionality reduction with adaptive filtering, hypothesizing that this integration will yield better results compared to conventional methods that rely solely on adaptive filtering. The objective is to demonstrate that by reducing dimensionality before applying adaptive filtering, the SNR (signal-to-noise ratio) can be significantly improved. Simulation results on high-dimensional datasets in remote sensing and biomedical signal applications confirm that the proposed hybrid approach outperforms baseline techniques, showing higher robustness and efficiency in both noise suppression and computational load [8-11]. This research highlights key contributions in the field, focusing on the advantages of incorporating dimensionality reduction as a preprocessing stage in high-dimensional signal processing [12-15]. Future work may involve fine-tuning these algorithms for real-time applications in various domains.

Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection

In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline encompassing data collection, preprocessing, feature selection, feature extraction, model development, and rigorous evaluation. Our dataset, derived from real-world banking customer profiles, was processed using normalization, encoding, and dimensionality reduction techniques. We implemented and compared eight models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM Hybrid, Autoencoder-Based Network, and Deep Neural Network (DNN). Among them, the Autoencoder-Based model achieved the highest accuracy of 91.56%, outperforming others in terms of segmentation clarity and computational efficiency. These findings suggest that deep learning methods, particularly Autoencoder-Based architectures, offer robust solutions for real-time banking customer segmentation, enabling institutions to tailor products and services more effectively.

The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.