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2 results about "Cluster sampling" patented technology
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Cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. The elements in each cluster are then sampled. If all elements in each sampled cluster are sampled, then this is referred to as a "one-stage" cluster sampling plan. If a simple random subsample of elements is selected within each of these groups, this is referred to as a "two-stage" cluster sampling plan. A common motivation for cluster sampling is to reduce the total number of interviews and costs given the desired accuracy. For a fixed sample size, the expected random error is smaller when most of the variation in the population is present internally within the groups, and not between the groups.
Non-gaussian weather radar signal adaptive spectral moment estimation method based on clustering algorithm
ActiveCN115407339BRadio wave reradiation/reflectionICT adaptationCluster algorithmExpectation–maximization algorithm
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.
Owner:BEIJING INST OF TECH +1
A financial transaction risk assessment method based on clustering sampling and meta-integration
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.
Owner:SOUTH CHINA UNIV OF TECH