Personalized Interpretable Classification

• URL: http://arxiv.org/abs/2302.02528v1
• Date: Mon, 6 Feb 2023 01:59:16 GMT
• Title: Personalized Interpretable Classification
• Authors: Zengyou He, Yifan Tang, Lianyu Hu, Mudi Jiang and Yan Liu
• Abstract summary: We make a first step towards formally introducing personalized interpretable classification as a new data mining problem. We conduct a series of empirical studies on real data sets. Our algorithm can achieve the same-level predictive accuracy as those state-of-the-art (SOTA) interpretable classifiers.
• Score: 8.806213269230057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to interpret a data mining model has received much attention recently, because people may distrust a black-box predictive model if they do not understand how the model works. Hence, it will be trustworthy if a model can provide transparent illustrations on how to make the decision. Although many rule-based interpretable classification algorithms have been proposed, all these existing solutions cannot directly construct an interpretable model to provide personalized prediction for each individual test sample. In this paper, we make a first step towards formally introducing personalized interpretable classification as a new data mining problem to the literature. In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample. To demonstrate the necessity, feasibility and advantages of such a personalized interpretable classification method, we conduct a series of empirical studies on real data sets. The experimental results show that: (1) The new problem formulation enables us to find interesting rules for test samples that may be missed by existing non-personalized classifiers. (2) Our algorithm can achieve the same-level predictive accuracy as those state-of-the-art (SOTA) interpretable classifiers. (3) On a real data set for predicting breast cancer metastasis, such a personalized interpretable classifier can outperform SOTA methods in terms of both accuracy and interpretability.

Related papers

• Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [59.90429949214134]
  We introduce a novel approach called textbfTwice textbfClass textbfBias textbfCorrection (textbfTCBC) We estimate the class bias of the model parameters during the training process. We apply a secondary correction to the model's pseudo-labels for unlabeled samples.
  arXiv  Detail & Related papers  (2023-12-27T15:06:36Z)
• Understanding and Mitigating Classification Errors Through Interpretable Token Patterns [58.91023283103762]
  Characterizing errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors. We propose to discover those patterns of tokens that distinguish correct and erroneous predictions. We show that our method, Premise, performs well in practice.
  arXiv  Detail & Related papers  (2023-11-18T00:24:26Z)
• Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis [9.05607520128194]
  This paper proposes a straightforward and cost-effective approach to assess whether a deep neural network (DNN) relies on the primary concepts of training samples. The proposed method does not require any test or generalization samples, only the parameters of the trained model and the training data that lie on the margin.
  arXiv  Detail & Related papers  (2023-06-10T11:20:04Z)
• fAux: Testing Individual Fairness via Gradient Alignment [2.5329739965085785]
  We describe a new approach for testing individual fairness that does not have either requirement. We show that the proposed method effectively identifies discrimination on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-10-10T21:27:20Z)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations [20.441578071446212]
  We introduce EVAL-X as a method to quantitatively evaluate interpretations and REAL-X as an amortized explanation method. We show EVAL-X can detect when predictions are encoded in interpretations and show the advantages of REAL-X through quantitative and radiologist evaluation.
  arXiv  Detail & Related papers  (2021-03-02T17:42:33Z)
• Identifying Wrongly Predicted Samples: A Method for Active Learning [6.976600214375139]
  We propose a simple sample selection criterion that moves beyond uncertainty. We show state-of-the-art results and better rates at identifying wrongly predicted samples.
  arXiv  Detail & Related papers  (2020-10-14T09:00:42Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach [9.978961706999833]
  We consider the problem of exploring the prediction level sets of a classifier using probabilistic programming. We define a prediction level set to be the set of examples for which the predictor has the same specified prediction confidence. We demonstrate this technique with experiments on a synthetic dataset and MNIST.
  arXiv  Detail & Related papers  (2020-01-09T15:57:51Z)

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.