Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests
Summary: Index-aware construction of decision trees and random forests that produces leaf predicates as axis-aligned hyperrectangles, turning inference into multidimensional range queries executed via existing indexes. Enables interactive search-by-classification over hundreds of millions of records in seconds without full scans. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 770 | A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search | 2021 | VLDB | 0.00016917602 |
| 826 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00016224841 |
| 1,107 | SPRINT: A Scalable Parallel Classifier for Data Mining | 1996 | VLDB | 0.00013985717 |
| 2,630 | PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce | 2009 | VLDB | 8.4128091e-05 |
| 2,687 | BOAT—Optimistic Decision Tree Construction | 1999 | SIGMOD | 8.3050259e-05 |
| 6,688 | REDS: Rule Extraction for Discovering Scenarios | 2021 | SIGMOD | 4.9623586e-05 |
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