Robust SMOTE-enhanced intuitionistic fuzzy LSTSVM for imbalanced and noisy data classification

SVED-SMOTE: support vector enhanced density SMOTE oversampling technique for imbalanced data classification

ASWBoost: Classification algorithm for noisy and imbalanced data based on parametric exponential loss

AdaBoost, a classical boosting ensemble algorithm, is widely applied for its strong classification performance. However, its standard exponential loss is highly sensitive to outliers, prone to overfitting, and inherently biased toward the majority class under class-imbalanced settings, degrading overall performance. To address these limitations, ASWBoost, a robust boosting algorithm, is proposed by introducing a parameter θ to modify the exponential loss. New update rules for base classifier weights and training sample probability distributions are derived, enabling adaptive adjustment of sample weights. The training error upper bound of the proposed algorithm is theoretically proven, and the impact of θ on its convergence properties is rigorously analyzed. Extensive experiments on synthetic and OpenML datasets demonstrate robust performance across Balanced Accuracy, G-mean, Macro-F1, and AUC. Critically, statistical analyses confirm that ASWBoost significantly outperforms AdaBoost, CS-AdaBoost, GBDT, SMOTEBoost, and RUSBoost, with overall performance comparable to XGBoost.

Intuitionistic Fuzzy Least Square Projection Twin Support Vector Machine for Pattern Classification

The Least Square Projection Twin Support Vector Machine (LSPTSVM) is an effective machine learning tool for solving classification problems. However, LSPTSVM does not account for the contribution of each sample during training, making it susceptible to outliers and noise. This susceptibility diminishes its generalization capability. To remedy this shortcoming, this paper introduces an Intuitionistic Fuzzy LSPTSVM (IFLSPTSVM). This model combines the LSPTSVM with the concept of Intuitionistic Fuzzy Numbers (IFN). In the training process of IFLSPTSVM, the importance of each training sample is gauged using an IFN-based score function that considers its geometric position and surrounding environment. Moreover, the weighted class mean, as opposed to the standard mean used in LSPTSVM, is employed in the calculation of intra-class scatter based on the intuitionistic fuzzy score of the sample. This approach effectively mitigates the impact of noise and outliers and more accurately captures the global information of the class samples. Experimental results on several real-world UCI benchmark datasets and the Case Western Reserve University rolling bearing datasets exhibit the efficacy of the proposed method.

A Novel Entropy-Based Framework for Hybrid Sampling in Imbalanced Learning

: Imbalanced data remain a critical challenge in classification, as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes, which are often the most critical. To address this issue, this paper proposes the Information Filtered Hybrid Algorithm (IF-HA), a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory. IF-HA quantifies instance importance through an instance-wise difference statistic. In the undersampling stage, majority of instances with low difference statistics in the border area are eliminated, while in the oversampling stage, synthetic samples are generated from two minority core points or two minority instances with high difference statistics located in the border area. This process removes noise, eliminates redundant majority border points, and generates synthetic minority samples in informative regions until an entropy-based imbalance threshold is reached. The proposed algorithm is evaluated on 20 benchmark datasets from the UCI and KEEL repositories. Results demonstrate that IF-HA consistently improves minority detection and achieves higher F1 Scores, recall, and AUC (Area Under the Curve) than other methods, including SMOTE, Borderline-SMOTE, ADASYN (Adaptive Synthetic Sampling), and SMOTE-TLNN-DEPSO. A real-world tuberculosis (TB) dataset from Indonesia was further used to validate the practical applicability of IF-HA using KNN, Random Forest, and XGBoost (eXtreme Gradient Boosting) classifiers. The results show consistent improvements after applying IF-HA. These findings indicate that entropy-based hybrid sampling is a promising approach for structured tabular imbalanced classification, while further validation on high-dimensional text and image datasets remains necessary to establish broader generalizability.

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

Extensive experiments conducted on UCI and KEEL benchmark datasets demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches.

Fuzzy Random Forest: Integrating Fuzzy Set Theory for Enhanced Imbalanced Classification

Standard Random Forest algorithms assume crisp class boundaries and precise feature values, limitations that become critical when dealing with ambiguous or overlapping data patterns common in imbalanced datasets. This paper presents Fuzzy Random Forest (FRF), a novel ensemble method that integrates fuzzy set theory into the tree construction process. Unlike existing methods that rely on crisp partitions, FRF extends the classical Gini impurity and Shannon entropy to fuzzy-valued impurity measures. Rather than counting instances as belonging entirely to one class, FRF weights each instance’s contribution to a node by its fuzzy membership degree, so that the resulting fuzzy class probabilities, and the fuzzy impurity and information-gain measures built from them, capture the ambiguity of borderline cases instead of forcing a crisp assignment. We provide complete mathematical derivations, including proofs of boundedness, crisp reduction, and membership conservation properties. Empirical evaluation on eight imbalanced datasets demonstrates that FRF consistently outperforms standard Random Forest, with particularly notable performance on datasets with high uncertainty and ambiguous class boundaries. On a credit risk dataset (80% non-default, 20% default), FRF achieves exceptional precision (0.99) for minority class predictions while maintaining 89.00% overall accuracy. Statistical validation using McNemar’s Test confirms FRF’s superiority over standard Random Forest, with a chi-square statistic of 100.0 (p less than 0.0001), a 20.6% improvement in information gain, and a 42.9% reduction in entropy reduction variance. FRF is particularly suited for applications requiring high precision in minority class identification and interpretable fuzzy decision rules, such as medical diagnosis, fraud detection, and credit risk assessment where regulatory compliance demands transparent decision-making.