Recognizing anomalies is an extremely important process in data analysis, aimed at identifying patterns in data that deviate from known norms or typical standards. These anomalies are often indicative of significant, and sometimes critical, issues such as fraud, network intrusions, and system failures. Traditional anomaly detection algorithms primarily focus on the attributes of individual observations within a dataset, typically establishing a ‘normal’ profile and flagging deviations from this profile as anomalies. This paper introduces an innovative enhancement to the Isolation Forest algorithm, a renowned method for anomaly detection known for its effectiveness and efficiency, especially in large datasets. The Isolation Forest algorithm operates by randomly partitioning the data space and constructing a binary tree, where the oddity score of a data point is ascertained based on its separation from the extremity to the base of the structure, enabling the autonomous detection of outliers in a completely unsupervised manner. The methodology presented in the paper is based on repeatedly building Isolation Forest models on datasets from which individual attributes are excluded. In our research, we used the SHAP (SHapley Additive exPlanations) method which comes from game theory and is used to determine the impact of individual features on the result of the model. When training the Isolation Forest on the full dataset, the SHAP method is used to obtain the coefficients of influence of model attributes on the prediction result. Both negative and positive influences are considered significant when counting the anomaly score. On the foundations of the results from all sub-models, a weighted average is calculated, to which weights are calculated based on the SHAP model. The comparative analysis of evaluation metrics revealed a substantial enhancement attributed to the implemented methodology. The metrics used for evaluation have shown improvement in most cases from 3.5 to 6 percent point. One of the metrics have shown an improvement of 12 percent. Obtained results demonstrate that this integrated approach not only enhances the prediction accuracy of the Isolation Forest algorithm but also offers a more interpretable understanding of the data. This advancement in anomaly detection methodology promises significant implications for various fields where quick and accurate detection of outliers is paramount.

This paper presents a novel approach to federated learning based on the Smooth Ordered Weighted Averaging (OWA) operator which enables flexible and context-sensitive weighting of local models during the aggregation process. To enhance the precision of the aggregated weight computations, we...

Ordered Weighted Averaging (OWA) operators are widely used as flexible rank-based aggregation mechanisms, including in ensemble classification where class-membership degrees produced by multiple models must be fused into a single decision score. This paper proposes an interpolated OWA framework that replaces the discrete summation by a numerical integration of a continuous surrogate constructed from the rank-wise products wix(i). The surrogate is obtained via interpolation on a normalized rank grid, and the integral is approximated using the composite trapezoidal rule. The proposed operators are evaluated in application to probability fusion in ensemble classification. Three interpolation schemes are investigated: Barycentric polynomial interpolation, cubic splines, and the shape-preserving Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). In addition, a trapezoidally smoothed baseline (Smooth OWA) and its interpolated extension are evaluated to separate the effect of local smoothing from interpolation. Experiments were conducted on a heterogeneous benchmark suite including binary and multiclass datasets ranging from 2 to 26 classes, with substantial variation in size and class imbalance. The proposed interpolated OWA yields consistent and frequently statistically significant accuracy improvements over the baseline OWA, increasing the benchmark-level mean accuracy, with the largest dataset-level gains reaching approximately 1.56 percentage point. The interpolated Smooth OWA variants provide additional, smaller yet stable improvements on top of quadrature-inspired smoothing. These results indicate that interpolation-based integration is a simple, modular, and robust refinement of rank-based aggregation for ensemble learning.

This paper introduces a procedure that transforms multiple evaluation metrics into a single aggregated score, providing a comprehensive and interpretable summary of machine learning performance. The approach is demonstrated on a set of metrics obtained from various anomaly detection algorithms...