Determination of class-specific variables in nonparametric multiple-class classification

• URL: http://arxiv.org/abs/2205.03623v1
• Date: Sat, 7 May 2022 10:08:58 GMT
• Title: Determination of class-specific variables in nonparametric multiple-class classification
• Authors: Wan-Ping Nicole Chen, Yuan-chin Ivan Chang
• Abstract summary: We propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class. We report the properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As technology advanced, collecting data via automatic collection devices become popular, thus we commonly face data sets with lengthy variables, especially when these data sets are collected without specific research goals beforehand. It has been pointed out in the literature that the difficulty of high-dimensional classification problems is intrinsically caused by too many noise variables useless for reducing classification error, which offer less benefits for decision-making, and increase complexity, and confusion in model-interpretation. A good variable selection strategy is therefore a must for using such kinds of data well; especially when we expect to use their results for the succeeding applications/studies, where the model-interpretation ability is essential. hus, the conventional classification measures, such as accuracy, sensitivity, precision, cannot be the only performance tasks. In this paper, we propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class such that we can have more information about its classification rule and the character of each class as well. The proposed method can have its prediction power approximately equal to that of the Bayes rule, and still retains the ability of "model-interpretation." We report the asymptotic properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations. We also separately discuss the variable identification, and training sample size determination, and summarize those procedures as algorithms such that users can easily implement them with different computing languages.

Related papers

• Probabilistic Safety Regions Via Finite Families of Scalable Classifiers [2.431537995108158]
  Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. We introduce the concept of probabilistic safety region to describe a subset of the input space in which the number of misclassified instances is probabilistically controlled.
  arXiv  Detail & Related papers  (2023-09-08T22:40:19Z)
• Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties [62.997667081978825]
  In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
  arXiv  Detail & Related papers  (2021-12-15T18:56:39Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• When in Doubt: Improving Classification Performance with Alternating Normalization [57.39356691967766]
  We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution. We empirically demonstrate its effectiveness across a diverse set of classification tasks.
  arXiv  Detail & Related papers  (2021-09-28T02:55:42Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Predicting Classification Accuracy When Adding New Unobserved Classes [8.325327265120283]
  We study how a classifier's performance can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. We formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes.
  arXiv  Detail & Related papers  (2020-10-28T14:37:25Z)
• Signal classification using weighted orthogonal regression method [0.0]
  This paper proposes a new classification method that exploits the intrinsic structure of each class through the corresponding Eigen components. The proposed method involves the obtained Eigenvectors by SVD of data from each class to select the bases for each subspace. It considers an efficient weighting for the decision-making criterion to discriminate two classes.
  arXiv  Detail & Related papers  (2020-10-12T19:12:14Z)
• Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods [8.870380386952993]
  Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation. We highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets.
  arXiv  Detail & Related papers  (2020-08-25T00:00:23Z)
• High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model [101.74172837046382]
  We propose a novel quadratic classification technique, the parameters of which are chosen such that the fisher-discriminant ratio is maximized. Numerical simulations show that the proposed classifier not only outperforms the classical R-QDA for both synthetic and real data but also requires lower computational complexity.
  arXiv  Detail & Related papers  (2020-06-25T12:00:26Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

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.