Name
Abstract | ||
|---|---|---|
In machine learning a coreset is defined as a subset of the training set using which an algorithm obtains performances similar to what it would deliver if trained over the whole original data. Advantages of coresets include improving training speed and easing human understanding. Coreset discovery is an open line of research as limiting the training might also impair the quality of the result. Differently, virtual points, here called archetypes, might be far more informative for a machine learning algorithm. Starting from this intuition, a novel evolutionary approach to archetype set discovery is presented: starting from a population seeded with candidate coresets, a multi-objective evolutionary algorithm is set to modify them and eventually create archetype sets, to minimize both number of points in the set and classification error. Experimental results on popular benchmarks show that the proposed approach is able to deliver results that allow a classifier to obtain lower error and better ability of generalizing on unseen data than state-of-the-art coreset discovery techniques.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3326789 | GECCO |
Keywords | Field | DocType |
Archetype sets, Classification, Coresets, Coreset discovery, Evolutionary algorithms, Explain AI, Machine learning, Multi-objective | Computer science,Archetype,Artificial intelligence,Machine learning,Coreset | Conference |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pietro Barbiero | 1 | 3 | 1.20 |
| Giovanni Squillero | 2 | 992 | 103.07 |
| Alberto Paolo Tonda | 3 | 127 | 20.85 |