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Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on Rough Set Theory
In this paper it is considered rule reduct generation problem, based on ...
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DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
Budgeted pruning is the problem of pruning under resource constraints. I...
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Indexing Metric Spaces for Exact Similarity Search
With the continued digitalization of societal processes, we are seeing a...
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MBGD-RDA Training and Rule Pruning for Concise TSK Fuzzy Regression Models
To effectively train Takagi-Sugeno-Kang (TSK) fuzzy systems for regressi...
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Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in...
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An Efficient Approach for Super and Nested Term Indexing and Retrieval
This paper describes a new approach, called Terminological Bucket Indexi...
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A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees
In fruit tree growth, pruning is an important management practice for pr...
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Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study
We focus on low-dimensional non-metric search, where tree-based approaches permit efficient and accurate retrieval while having short indexing time. These methods rely on space partitioning and require a pruning rule to avoid visiting unpromising parts. We consider two known data-driven approaches to extend these rules to non-metric spaces: TriGen and a piece-wise linear approximation of the pruning rule. We propose and evaluate two adaptations of TriGen to non-symmetric similarities (TriGen does not support non-symmetric distances). We also evaluate a hybrid of TriGen and the piece-wise linear approximation pruning. We find that this hybrid approach is often more effective than either of the pruning rules. We make our software publicly available.
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