Outlier detection has garnered considerable attention in recent years due to its wide-ranging applications across various research domains. This surge in interest has led to the development of numerous detection techniques, predominantly based on distance or density metrics. A notable limitation of these existing methods is their reliance on parameter adjustments, significantly affecting the outcome. Additionally, these methods exhibit intrinsic flaws: distance-based approaches struggle with clusters with varying local densities, while density-based methods fail to identify patterns within low-density areas. Moreover, most prior techniques are adept at identifying only one kind of outlier—local, global, or group of outliers. Addressing these challenges, we introduce the Adaptive Radius Density-Based Outlier Detection (ARDOD) method, which departs from the traditional parameter-dependent approach. ARDOD is a novel parameter-free algorithm that dynamically determines the necessary parameters based on the data distribution within the feature space. This innovative method demonstrates robust performance in detecting all three categories of outliers. The efficacy and superior performance of ARDOD are validated through an extensive experimental analysis involving various synthetic and real-world datasets. This analysis showcases ARDOD's advantages over seven established methods: Local Outlier Factor (LOF), Angle-Based Outlier Detection (ABOD), Robust Distance-Based Outlier Score (RDOS), Directed density ratio Changing Rate-based outlier detection (DCROD), Empirical-Cumulative-distribution-based Outlier Detection(ECOD), mean-shift outlier detector(MOD +),and Local–Global Outlier Detection (LGOD), underscoring its potential as a versatile tool in outlier detection research.

In classification, one of the common problems is the class imbalance problem. This phenomenon that is growing significance emerges in most real fields and occurs when data samples are distributed among classes unevenly. This means that most of the data are in the larger class, and there are fewer data in the smaller class. Since standard classifiers do not consider the distribution of imbalanced class, they indicate undesirable behavior in facing them. Many techniques have been proposed to solve the problem of class imbalance. Among these methods, a group called preprocessing techniques tries to create a balance between training sets. These methods balance the classes' distribution by removing redundant samples from the larger class or creating new samples for the smaller one. The first group is known as under-sampling, and the second one is known as over-sampling techniques. In this paper, we propose a score-based preprocessing technique based on both under-sampling and over-sampling to overcome the weakness of classifiers in class imbalance problems. For this purpose, we apply the sharing strategy in both stages to determine more suitable samples based on their importance in the feature space. In the over-sampling stage, the smaller class's synthetic samples are generated by interpolating between more sparse samples. After that, in the under-sampling stage, denser samples of the larger class are selected to be removed. We use the binary tournament selection operator in both stages to perform over-sampling and under-sampling based on probabilities. In experiments, the support vector machine (SVM) is employed to train a classification model from the balanced training sets obtained by different preprocessing methods. Besides, F-measure and AUC measures are considered as evaluation tools. At the last step, we compare all methods in terms of the classification model's complexity. According to the results obtained from 44 standard imbalanced datasets, the proposed method's superiority and effectiveness compared to other methods have been revealed.

Classification is one of the most popular branches of supervised learning algorithms. In the real-world problems, there are some situations in which distributions of the two classes are not the same. This situation is known as a class imbalanced problem. In the past years, several investigations have been done to find a way of handling imbalanced data, which most of them stay in one of two groups, including internal techniques and external techniques. The proposed gravitational density-based mass sharing method (GDMS) is an internal method that is designed based on k-nearest neighbor and fixed radius nearest neighbor (FRNN) rules. GDMS is a new technique that assigns masses to instances based on their local density while considering the global information too. In the labeling phase, GDMS decides based on the sum of gravitational forces coming from the candidates set, which are defined by FRNN rule. The GDMS does not need any parameters to be set in the whole procedure of classification, which is an advantage in comparison with the previous methods. To demonstrate the effectiveness of our proposed method, we use 40 standard datasets from the KEEL repository. The results show the effectiveness and superiority of GDMS compared to the competing methods.