A review of instance selection methods

By J. A. Olvera-López, J. A. Carrasco-Ochoa, José Francisco Martínez Trinidad, Josef Kittler. 2010

401 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Evaluation & Benchmarks, Statistical Learning Theory

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, methodology): “We used a combination of a wrapper method using selection criterion based on the classifier’s accuracy [40] and a simple filter based on the image similarity measurement.”
Dataset Distillation (2018, background): “Similarly, core-set construction (Tsang et al., 2005; Har-Peled & Kushal, 2007; Bachem et al., 2017; Sener & Savarese, 2018) and instance selection (Olvera-López et al., 2010) methods aim to select a subset of the entire training data, such that models trained on the subset will perform as well as…”
Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “This task differs from approaches to instance selection [28], based on genetic algorithms [36] (also specialized for decision trees [40]), in that their objective is to select a subset of instances from an available training set.”
Selecting Representative Data Sets (2012, methodology): “A good review of instance selection methods is in [65, 71].”
Recent advances and emerging challenges of feature selection in the context of big data (2015, background): “, redundant or irrelevant, samples [101], feature selection can scale machine learning algorithms by reducing input dimensionality and therefore algorithm run-time.”
Flexible Dataset Distillation: Learn Labels Instead of Images (2020, background): “In this sense it is related to other endeavours such as dataset pruning [2, 10, 16], core-set construction [29, 3, 26] and instance selection [22] that focuses on dataset summarization through a small number of examples.”
Dataset Distillation by Matching Training Trajectories (2022, methodology): “Similar to dataset distillation, coreset [1, 4, 13, 34, 41] and instance selection [28] aim to select a subset of the entire training dataset, where training on this small subset achieves good performance.”
Instance Selection for GANs (2020, methodology): “In this paper, we analyze the use of instance selection [20] in the generative setting.”
A Survey on Curriculum Learning (2021, background): “At a more abstract level, a curriculum can be seen as a sequence of (binary) instance selection [80] or (soft) example reweighting along the training process to achieve faster convergence or better generalization, which is beyond the “easy to hard” or “starting small” principles.”
Uncertainty in big data analytics: survey, opportunities, and challenges (2019, methodology): “By utilizing instance selection, it is possible to reduce training sets and runtime in the classification or training phases [62].”
Factual and Counterfactual Explanations for Black Box Decision Making (2019, methodology): “This task differs from approaches to instance selection based on genetic algorithms.(7) In our case, we cannot assume the availability of the training set of b, or not even that b is a supervised ML predictor for which a training set exists.”
On The Stability of Interpretable Models (2018, background): “Feature selection (FS) [30] and instance selection (IS) [31] are beneficial in removing noise and redundancies, in reducing the data collection effort, in balancing the data distribution, in speeding up model learning.”
Big data preprocessing: methods and prospects (2016, background): “Thus, instance selection is comprised by a series of techniques that must be able to choose a subset of data that can replace the original data set and also being able to fulfill the goal of a data mining application [33, 34].”