PPFS: Predictive Permutation Feature Selection

Hassan, Amjad; Paik, Jiaul H.; Khare, Swanand; Hassan, Syed Asif

doi:10.48550/arxiv.2110.10713

Cited by 3 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It works by randomly permuting the values of each feature and measuring the decrease in model performance. This method provides a more accurate measure of feature importance but can be computationally expensive for large datasets (Hassan et al, 2021;Chamma et al, 2023;Fumagalli et al, 2023). Random Forest features are useful in several ways.…”

Section: Random Forest Feature Importancesmentioning

confidence: 99%

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Claudette,

Roger

2024

jaar

View full text Add to dashboard Cite

This study aimed to identify key CMD-related factors affecting Cameroon cassava farmers’ incomes originating from both the sale of cassava cuttings (V215) and the sale of cassava roots (V216). To achieve this, nine CMD-related variables were used to independently train two Random Forest models. These models were later employed for regression-based prediction of both financial targets V215 and V216. The Random Forest (RF)-based mean absolute percentage error for targets V215 and V216 were 0.19 and 1.25 respectively. The RF-based mean Gaussian deviance for targets V215 and V216 were 0.07 and 0.51 respectively. Based on RF feature importance scores (RFFI), the top 3 factors affecting income originating from the sale of cassava cuttings were found to be: late appearance of symptoms as a difficulty associated with regular field monitoring (RFFI of 0.2594), removal of infected plants as a method of controlling frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1633) and lack of healthy planting material due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1495). Also, the top 3 factors affecting income originating from the sale of cassava roots were found to be: the replacement of infected plants with healthy cuttings as a method of controlling the frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1974), decrease in yield due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1530) and poor plant growth due to frequent occurrence of viral diseases in respondents’ cassava fields (RFFI of 0.1388).

show abstract

Section: Random Forest Feature Importancesmentioning

confidence: 99%

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Claudette,

Roger

2024

jaar

View full text Add to dashboard Cite

show abstract

“…16 Descriptions of these methods are presented in ►Supplementary Appendix B (available in the online version). PyImpetus, 17 another causal feature selection model, was also evaluated. PyImpetus employs a modified IAMB algorithm using predictive permutation impact instead of conditional mutual information.…”

Section: Feature Selectionmentioning

confidence: 99%

Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

et al. 2023

View full text Add to dashboard Cite

Background Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced by specific feature selection methods are more robust to temporal dataset shift as measured by out-of-distribution (OOD) performance, while maintaining in-distribution (ID) performance. Methods Our dataset consisted of intensive care unit patients from MIMIC-IV categorized by year groups (2008–2010, 2011–2013, 2014–2016, and 2017–2019). We trained baseline models using L2-regularized logistic regression on 2008–2010 to predict in-hospital mortality, long length of stay (LOS), sepsis, and invasive ventilation in all year groups. We evaluated three feature selection methods: L1-regularized logistic regression (L1), Remove and Retrain (ROAR), and causal feature selection. We assessed whether a feature selection method could maintain ID performance (2008–2010) and improve OOD performance (2017–2019). We also assessed whether parsimonious models retrained on OOD data performed as well as oracle models trained on all features in the OOD year group. Results The baseline model showed significantly worse OOD performance with the long LOS and sepsis tasks when compared with the ID performance. L1 and ROAR retained 3.7 to 12.6% of all features, whereas causal feature selection generally retained fewer features. Models produced by L1 and ROAR exhibited similar ID and OOD performance as the baseline models. The retraining of these models on 2017–2019 data using features selected from training on 2008–2010 data generally reached parity with oracle models trained directly on 2017–2019 data using all available features. Causal feature selection led to heterogeneous results with the superset maintaining ID performance while improving OOD calibration only on the long LOS task. Conclusions While model retraining can mitigate the impact of temporal dataset shift on parsimonious models produced by L1 and ROAR, new methods are required to proactively improve temporal robustness.

show abstract

“…Predictive Permutation Feature Selection (PPFS) 14 is a novel feature selection algorithm based on the concept of Markov Blanket (MB). MB can be described by the following Fig.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The PPFS algorithm selects a subset of features based on their performance both individually and as a group, and it can automatically decide how many features to take and tries to nd the optimal combination of features 14 . The PPFS algorithm is implemented by using the PPIMBC function in the PyImeptus package for python.…”

Section: Feature Selectionmentioning

confidence: 99%

Machine Learning Soft Voting Algorithm for Prediction and Detection of Nonalcoholic Fatty Liver Disease

Cao

Zhang

2022

Preprint

View full text Add to dashboard Cite

Nonalcoholic fatty liver disease (NAFLD) is one of the most commonly diagnosed chronic liver diseases in the world and has become an essential public health problem. Introduction of machine learning algorithms to evaluate the best predictive clinical model for NAFLD. In this paper, this study proposes a machine learning Voting algorithm with Genetic Algorithm, Neural Network, Random Forest, and Logistic Regression for NAFLD detection and diagnosis. First, 2,522 of the 10,508 samples met the diagnostic criteria for NAFLD. Visualizing the distribution of missing values, and KNN algorithm is used to fill the missing values. Doing Kolmogorov-Smirnov Z test and the heatmap of 19 variables. The PPFS feature selection method is used to perform the feature selection and the final 11 features are retained. Alanine aminotransferase (ALT), body mass index (BMI), triglycerides (TG), γ-glutamyl transpeptidase (γGT), and Low-density lipoprotein cholesterol (LDL) were the top 5 features contributing to NAFLD. 10 basic machine learning algorithms were used, and the four machine learning algorithms with the highest accuracy were Genetic Algorithm, Neural Network, Random Forest, and Logistic Regression. These four algorithms are fused into the proposed Voting algorithm through the Soft Voting method of Ensemble learning. 10-fold cross-validation was used in the classification. To verify the proposed Voting algorithm, it is compared with other 10 basic machine learning algorithms It achieved accuracy, recall, precision, \({F}_{1}\) score, AUC of up to 0.846212, 0.573248, 0.725806, 0.640569, 0.894010, respectively. According to the results, the proposed Voting algorithm demonstrated the best performance.

show abstract

PPFS: Predictive Permutation Feature Selection

Cited by 3 publications

References 12 publications

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

A random forest-based analysis of cassava mosaic disease-related factors affecting the on-farm livelihoods of cassava farmers

Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

Machine Learning Soft Voting Algorithm for Prediction and Detection of Nonalcoholic Fatty Liver Disease

Contact Info

Product

Resources

About