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Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning
Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences.
Selective Inference for Sparse High-Order Interaction Models
Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e. g., $> 10^{17}$).
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
The main obstacle of this problem is in the difficulty of taking into account the selection bias, i. e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases.
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model.
Safe Feature Pruning for Sparse High-Order Interaction Models
An SFS rule has a property that, if a feature satisfies the rule, then the feature is guaranteed to be non-active in the LASSO solution, meaning that it can be safely screened-out prior to the LASSO training process.
