# Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning

arXiv: https://arxiv.org/abs/2306.13561 · HTML: https://mlsanity.com/paper/2306.13561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 63 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Gap Safe screening rules for sparsity enforcing penalties](https://mlsanity.com/paper/1611.05780/) (2016)
- [Mind the duality gap: safer rules for the Lasso](https://mlsanity.com/paper/1505.03410/) (2015)
- [Fast OSCAR and OWL Regression via Safe Screening Rules](https://mlsanity.com/paper/2006.16433/) (2020)
- [Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling](https://mlsanity.com/paper/1602.02485/) (2016)
- [Screening Tests for Lasso Problems](https://mlsanity.com/paper/1405.4897/) (2014)
- [Fast and More Powerful Selective Inference for Sparse High-order Interaction Model](https://mlsanity.com/paper/2106.04929/) (2021)
- [An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification](https://mlsanity.com/paper/2208.06058/) (2022)
- [Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining](https://mlsanity.com/paper/1602.04548/) (2016)
- [Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso](https://mlsanity.com/paper/1412.4080/) (2014)
- [Screening Rules for Lasso with Non-Convex Sparse Regularizers](https://mlsanity.com/paper/1902.06125/) (2019)
- [Safe Grid Search with Optimal Complexity](https://mlsanity.com/paper/1810.05471/) (2018)
- [Expanding boundaries of Gap Safe screening](https://mlsanity.com/paper/2102.10846/) (2021)
- [Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems](https://mlsanity.com/paper/1504.02870/) (2015)
- [Regularization Path of Cross-Validation Error Lower Bounds](https://mlsanity.com/paper/1502.02344/) (2015)
- [Safe Triplet Screening for Distance Metric Learning](https://mlsanity.com/paper/1802.03923/) (2018)
- [Learning Hierarchical Interactions at Scale: A Convex Optimization Approach](https://mlsanity.com/paper/1902.01542/) (2019)
- [Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union](https://mlsanity.com/paper/2010.15377/) (2020)
- [WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models](https://mlsanity.com/paper/1802.05980/) (2018)
- [Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening](https://mlsanity.com/paper/1810.01683/) (2018)
- [Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization](https://mlsanity.com/paper/1602.04579/) (2016)

## Built on by (most cited first, 2 of 2)

- [Distributionally Robust Safe Screening](https://mlsanity.com/paper/2404.16328/) (2024)
- [Distributionally Robust Safe Sample Elimination under Covariate Shift](https://mlsanity.com/paper/2406.05964/) (2024)

## Abstract (arXiv)

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 behind predictive pattern mining is to build a prediction model by considering substructures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the Safe Pattern Pruning (SPP) method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences.