A Complexity Measure for Binary Classification Problems Based on Lost Points

Lancho, Carmen; Diego, Isaac Martín de; Cuesta, Marina; Aceña, Víctor; Moguerza, Javier M.

doi:10.1007/978-3-030-91608-4_14

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…From the original work of Ho and Basu [14], several complexity measures have been proposed [29,16,25]. A recompilation of most of them can be found at [20], where complexity measures are classified into six groups: feature-based measures (focused on the overlap of features among classes), linearity measures (to determine the linear separability of the classes), neighborhood measures (to analyze the distribution and the overlap of the classes using the distance among points), network measures (to explore data modeled as a graph), dimensionality measures (to relate the number of samples to the number of dimensions) and class imbalanced measures (to evaluate the balance between the size of the classes).…”

Section: State-of-the-artmentioning

confidence: 99%

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Aceña,

Lancho,

de Diego

et al. 2024

Preprint

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Within the scope of Machine Learning (ML), Instance Selection (IS) is a sampling process that consists of filtering noise and removing redundant data. In classification problems, IS implies a compromise between maximizing performance and reducing the sample size used for training. Complexity measures provide relevant information about the difficulty of classifying instances, which makes them appropriate for IS since they can capture, for example, noisy or borderline points. This paper introduces the complexity measure DDN, defined at three levels: instance, class, and dataset. The Dynamic Disagreeing Neighbors (DDN) of an instance is defined as the percentage of its nearest neighbors that belong to other classes. The DDN is based on the Nearest Centroid Neighbors (NCN) neighborhood computation, which is dynamically adjusted to the data distribution. In addition, the distance of each neighbor is taken into account so that those farther away are less influential and those closer are more influential for the instance complexity. The validity of the proposal is evaluated through a series of experiments where it is compared with the widely known k-Disagreeing Neighbors (kDN) in terms of stability, correlation with classification error, and performance in IS. The DDN has shown competitive, stable, and robust results throughout the experiments, generally improving on those obtained with the alternative.

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Section: State-of-the-artmentioning

confidence: 99%

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Aceña,

Lancho,

de Diego

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, tracking the results from the different layers of the procedure provides useful information. Some promising results of a preliminary version of the proposal have been presented in [20].…”

Section: Introductionmentioning

confidence: 99%

Hostility measure for multi-level study of data complexity

et al. 2022

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Complexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. The proposal is built by estimating the novel notion of hostility: the difficulty of correctly classifying a point, a class, or a whole dataset given their corresponding neighborhoods. The proposed measure is estimated at the instance level by applying the k-means algorithm in a recursive and hierarchical way, which allows to analyze how points from different classes are naturally grouped together across partitions. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. The validity of the proposal is evaluated through a variety of experiments dealing with the three perspectives and the corresponding comparative with the state-of-the-art measures. Throughout the experiments, the hostility measure has shown promising results and to be competitive, stable, and robust.

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A Complexity Measure for Binary Classification Problems Based on Lost Points

Cited by 2 publications

References 8 publications

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Hostility measure for multi-level study of data complexity

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