Optimal probabilistic feature shifts for reclassification in tree ensembles

• URL: http://arxiv.org/abs/2412.03722v1
• Date: Wed, 04 Dec 2024 21:28:24 GMT
• Title: Optimal probabilistic feature shifts for reclassification in tree ensembles
• Authors: Váctor Blanco, Alberto Japón, Justo Puerto, Peter Zhang,
• Abstract summary: We show that the most viable changes for an observation to reach the desired class do not always coincide with the closest distance point. Individuals are endowed with a probability to change each of its features to a given value, which determines the overall probability of changing to the target class. Our method also allows us to rank the most important features in the tree-ensemble.
• Score: 0.5249805590164903
• License:
• Abstract: In this paper we provide a novel mathematical optimization based methodology to perturb the features of a given observation to be re-classified, by a tree ensemble classification rule, to a certain desired class. The method is based on these facts: the most viable changes for an observation to reach the desired class do not always coincide with the closest distance point (in the feature space) of the target class; individuals put effort on a few number of features to reach the desired class; and each individual is endowed with a probability to change each of its features to a given value, which determines the overall probability of changing to the target class. Putting all together, we provide different methods to find the features where the individuals must exert effort to maximize the probability to reach the target class. Our method also allows us to rank the most important features in the tree-ensemble. The proposed methodology is tested on a real dataset, validating the proposal.

Related papers

• Class-specific feature selection for classification explainability [0.0]
  This work first introduces a comprehensive review of the concept of class-specific, with a focus on feature selection and classification. The fundamental idea of the class-specific concept resides in the understanding that the significance of each feature can vary from one class to another. This class-specific perspective offers a more effective approach to classification tasks by recognizing and leveraging the unique characteristics of each class.
  arXiv  Detail & Related papers  (2024-11-02T10:31:55Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• Task-Specific Embeddings for Ante-Hoc Explainable Text Classification [6.671252951387647]
  We propose an alternative training objective in which we learn task-specific embeddings of text. Our proposed objective learns embeddings such that all texts that share the same target class label should be close together. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy.
  arXiv  Detail & Related papers  (2022-11-30T19:56:25Z)
• A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks [0.21485350418225244]
  This paper presents a general prediction model to hierarchical multi-label classification (HMC), where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice.
  arXiv  Detail & Related papers  (2022-03-23T17:29:17Z)
• Multi-Class Classification from Single-Class Data with Confidences [90.48669386745361]
  We propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. We show that our method can be Bayes-consistent with a simple modification even if the provided confidences are highly noisy.
  arXiv  Detail & Related papers  (2021-06-16T15:38:13Z)
• Dynamic Instance-Wise Classification in Correlated Feature Spaces [15.351282873821935]
  In a typical machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. A new method is proposed that sequentially selects the best feature to evaluate for each test instance individually, and stops the selection process to make a prediction once it determines that no further improvement can be achieved with respect to classification accuracy. The effectiveness, generalizability, and scalability of the proposed method is illustrated on a variety of real-world datasets from diverse application domains.
  arXiv  Detail & Related papers  (2021-06-08T20:20:36Z)
• Meta Learning for Few-Shot One-class Classification [0.0]
  We formulate the learning of meaningful features for one-class classification as a meta-learning problem. To learn these representations, we require only multiclass data from similar tasks. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario.
  arXiv  Detail & Related papers  (2020-09-11T11:35:28Z)
• Cautious Active Clustering [79.23797234241471]
  We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. Our approach is to consider the unknown probability measure as a convex combination of the conditional probabilities for each class.
  arXiv  Detail & Related papers  (2020-08-03T23:47:31Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective [98.70226503904402]
  Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions. We propose to augment the classic class-balanced learning by explicitly estimating the differences between the class-conditioned distributions with a meta-learning approach.
  arXiv  Detail & Related papers  (2020-03-24T11:28:42Z)
• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification [91.67977602992657]
  We propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training.
  arXiv  Detail & Related papers  (2020-03-20T15:44:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.