利用在线属性选择进行可解释的少量学习

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mohammad Reza Zarei, Majid Komeili
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引用次数: 0

摘要

少量样本学习(FSL)提出了一个具有挑战性的学习问题,即每个类别只有少量样本。与传统的分类方法相比,由于出错的几率更大,因此决策解释在少数几次分类中显得更为重要。然而,以往的 FSL 方法大多是黑箱模型。在本文中,我们提出了一种基于人类友好属性的 FSL 固有可解释模型。在此之前,人类友好属性已被用于训练具有人机交互和可解释性潜力的模型。但是,这种方法无法直接扩展到少量分类场景。此外,我们还提出了一种在线属性选择机制,以有效过滤掉每一集中的无关属性。属性选择机制通过减少参与每个事件的属性数量,提高了准确性并有助于提高可解释性。我们进一步提出了一种机制,可自动检测可用的人类友好属性库不足的情节,并随后通过使用一些学习到的未知属性来增加属性库。我们在四个广泛使用的数据集上证明,所提出的方法取得了与黑盒少量学习模型相当的结果。我们还根据经验评估了不同模型与人类理解之间的决策一致性水平,结果表明我们的模型优于基于这一标准的比较方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretable few-shot learning with online attribute selection
Few-shot learning (FSL) presents a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot classification due to a greater chance of error compared to traditional classification. However, the majority of the previous FSL methods are black-box models. In this paper, we propose an inherently interpretable model for FSL based on human-friendly attributes. Previously, human-friendly attributes have been utilized to train models with the potential for human interaction and interpretability. However, such approaches are not directly extendible to the few-shot classification scenario. Moreover, we propose an online attribute selection mechanism to effectively filter out irrelevant attributes in each episode. The attribute selection mechanism improves accuracy and helps with interpretability by reducing the number of attributes that participate in each episode. We further propose a mechanism that automatically detects the episodes where the pool of available human-friendly attributes is insufficient, and subsequently augments it by engaging some learned unknown attributes. We demonstrate that the proposed method achieves results on par with black-box few-shot learning models on four widely used datasets. We also empirically evaluate the level of decision alignment between different models and human understanding and show that our model outperforms the comparison methods based on this criterion.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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