6 citations · 7 across the 5 of their papers we have counts for
5 papers
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning
Takumi Yoshida, Hiroyuki Hanada, Kazuya Nakagawa +3
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. The main idea be…
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
Shinya Suzumura, Kazuya Nakagawa, Mahito Sugiyama +2
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the…
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contr…
Safe Feature Pruning for Sparse High-Order Interaction Models
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the numbe…
An Efficient Post-Selection Inference on High-Order Interaction Models
S. Suzumura, K. Nakagawa, K. Tsuda +1
Finding statistically significant high-order interaction features in predictive modeling is important but challenging task. The difficulty lies in the fact that, for a recent appli…