Impact Statement:Causal discovery is an important technique in data mining, which is widely used in various application fields such as stocks and power plants. At present, most causal dis...Show More
Abstract:
Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and...Show MoreMetadata
Impact Statement:
Causal discovery is an important technique in data mining, which is widely used in various application fields such as stocks and power plants. At present, most causal discovery algorithms can only process and analyze linear data, but the large amount of data generated in real life often presents complex nonlinear relationships. The independent detection method we present in this article solves this problem. After adopting our algorithm, the time efficiency and precision performance of causal discovery are significantly improved, and the algorithm is applied to the actual power plant data, which can well predict the future operation trend and give fault warning information according to the operation trend of the monitoring point in a period of time.
Abstract:
Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and high-dimensional data, this article proposes a new structural learning algorithm called spatial coordinates based (SCB). The SCB algorithm effectively discovers causal networks from large scale nonlinear data. In this article, we make three main contributions. Firstly, based on the Hilbert–Schmidt independence criterion (HSIC), we propose new independence test coefficients: spatial coordinate (SC) coefficient and conditional spatial coordinate (CSC) coefficient. We also prove that the statistical distribution of the CSC coefficient follows a standard normal distribution. Secondly, using the statistical distribution of the CSC coefficient, we redefine the correlation of variables and combine it with local learning to propose the SCB algorithm. Finally, we demonstrate the ef...
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 5, Issue: 8, August 2024)