Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial problem in anomaly detection, few solutions in anomaly detection are suitable for it at present. Recently, some researchers use GAN-based methods such as TAnoGAN and TadGAN to solve TSAD problem. However, problems such as model collapse, low generalization capability and poor accuracy still exist. In this article, we proposed a Dilated Convolutional Transformer-based GAN (DCT-GAN) to enhance accuracy and improve generalization capability of the model. Specifically, DCT-GAN utilize several generators and a single discriminator to alleviate the mode collapse problem. Each generator consists of a dilated convolutional neural network and a Transformer block to obtain fine-grained and coarse-grained information of the time series, which is a useful component to improve generalization capability. We also use weight-based mechanism to balance these generators. Experiments verify the effectiveness of our method and each part of DCT-GAN.

Anomaly detection has become a popular topic in many domains because anomalies can provide valuable information. Recently, ensemble learning has been applied to improve the generalization ability of existing anomaly detection methods. In an anomaly ensemble framework, diversity is essential for building a powerful ensemble method to obtain impressive performance. However, most studies use heuristic techniques to improve the diversity of ensembles, and it generally leads to limited diversity. To obtain improved diversity, we propose a diversity aware-based sequential ensemble method (called D-SEM) for anomaly detection. Specifically, our proposed method divides the ensemble diversity into two parts: sample diversity and model diversity. For sample diversity, we introduce the subsampling technique to implement preliminary generation of diverse datasets for training. For model diversity, we design an ensemble-based optimization model to learn base classifiers with improved diversity. Furthermore, an unsupervised diversity measure is proposed to quantitatively assess diversity and an anomaly pruning strategy is utilized to successively eliminate pseudo-anomalies. Based on the inclusion of sample diversity and model diversity, the proposed D-SEM method obtains better generalization ability for anomaly detection. The experimental results based on real-world datasets suggest that the proposed method has superior performance compared with various state-of-the-art methods.

Anomaly detection has been widely studied in many domains where abnormal samples with more valuable information than normal samples are of interest to researchers. Numerous methods have been developed for detecting anomalies, with one-class classification techniques being especially popular due to their ability to train using only normal samples. However, the presence of anomalies in the unlabeled data may negatively impact the accuracy of the model. Although some studies have attempted to improve robustness by removing potential anomalies or assigning lower weights to them during data preprocessing, these approaches are limited in effectiveness. This limitation arises because sample preprocessing is conducted independently of anomaly scoring, leading to the potential misclassification of anomalies as normal samples or their assignment of high weights, ultimately resulting in suboptimal detection performance. In this paper, an ensemble-based joint training framework is proposed for anomaly detection, called E-ORAD, to enhance robustness. E-ORAD jointly optimizes sample weights and anomaly scores within a unified ensemble model. Specifically, to compute the weight of each sample, a prior knowledge-based regularization term is incorporated into the objective function. Additionally, an anomaly score-based hinge loss function is developed to ensure that abnormal samples are assigned higher scores than normal samples. An alternating iteration method is then introduced to solve the proposed model. Experimental results on 10 real-world datasets demonstrate that E-ORAD outperforms other state-of-the-art methods in terms of detection performance.

Early breast cancer diagnosis is crucial for improving treatment outcomes for women. Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their performance. To tackle this issue, we propose a robust enhanced ensemble learning method (REEL). Specifically, a double-level over-sampling technology is developed to increase the diversity of synthesized minority breast cancer samples before model training, and an improved Random Forest is proposed to reconcile the bias and variance. In addition, a data-driven based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers. Experimental results on breast cancer datasets and 19 other imbalanced datasets validate that our method outperforms other algorithms in terms of accuracy, F1 score, and AUC.These findings confirm that our method can further improve classification accuracy and has significant application value in the diagnosis of breast cancer.

The investigation and evaluation data of reserved cultivated land resources are of great significance to the research and policy-making about cultivated land protection, it is necessary to establish stable and efficient information systems to manage and use the massive data. To this end, this article adopt a hybrid storage strategy, uses relational database, non-relational database and file database to establish the national investigation and evaluation database of reserved cultivated land resources. Automatic and parallel database building technology, spatial big data calculation technology and vector data dynamic rendering technology are researched on, which has solved the technical bottleneck of large-scale spatial data storage, management and visualization. On this basis, developed the database management system, it has been applied in the government administration of cultivated land resources in China and exerts positive results.

Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling’s expression using information theory match the leading terms in Keeton’s expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. While intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound.