Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting.

ABSTRACT: This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic inter-class distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics an

SUBMITTER: Ke D 

PROVIDER: S-EPMC11623274 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature