3 results about "Cluster sampling" patented technology

This paper presents a global soil moisture prediction method based on an average cluster sampling strategy, using group samples uniformly extracted from regions with heterogeneous soil moisture variation characteristics for training. Ensuring diversity in samples from different regions and uniformity in sample selection helps to stably learn features during model training. Experiments were conducted on the LandBench dataset, using five different seeds for a 1-day global forecast. The results for each seed were averaged, and the results show that the proposed group sampling strategy outperforms several traditional LSTM-based models that do not adopt this strategy, with a median R 2 Improvements range from 2.36% to 4.31%, while improvements from KGE range from 1.95% to 3.16%. Furthermore, at high latitudes, especially in specific regions, the proposed strategy demonstrates significant improvements in explanatory power, with R² improvements exceeding 40%. This validates the effectiveness of the proposed sampling strategy and introduces a new training paradigm to enhance generalization capabilities for the deep learning community.

The application discloses a non-Gaussian weather radar signal adaptive spectral moment estimation method based on a clustering algorithm. Firstly, a Gaussian mixture model is used to model a non-Gaussian power spectrum, and an elbow rule is used to calculate the number K of Gaussian power spectrums contained in the non-Gaussian signal. Then, a K-means clustering algorithm is used to cluster sampling points of the power spectrum of the non-Gaussian signal, and the mean and variance of the clustering are used as initial values of spectral moments. Finally, an expectation maximization algorithm is used to further estimate the spectral moments. The application can effectively estimate the spectral moment parameters when the power spectrum of a weather signal is in a non-Gaussian distribution and traditional spectral moment estimation methods fail.

This invention discloses a financial transaction risk assessment method based on clustering sampling and meta-ensemble. The method includes the following steps: constructing diverse training subsets through a sampling mechanism based on supervised fuzzy clustering, balancing the number of risky transactions and normal transactions within the subsets, and ensuring that the union of the subsets covers the original complete financial transaction dataset as much as possible; then training base classifiers; extracting meta-features by calculating indicators based on classification difficulty and model diversity, while considering classification difficulties caused by class overlap and the diversity of base classifiers to make ensemble selection judgments; constructing a meta-aggregator based on self-attention networks and convolutional neural networks, enabling it to consider the relative performance of multiple base classifiers simultaneously, rather than simply assigning weights to individual base classifiers. This invention demonstrates better performance in improving the model's ability to identify risky transactions while minimizing its impact on the model's predictive ability for normal transactions.