基于归属的可解释分类神经网络,具有全球和本地视角
Zihao Shi1, Zuqiang Meng2, Haiming Tuo1
1Guangxi University, College of Computer, Electronics and Information, Nanning, 530004, China.
Scientific reports
|July 9, 2025
概括
本研究引入了一个可解释的神经网络,用于表格数据,平衡性能和可解释性. 这种基于归因的新型模型实现了高精度,同时为可靠的AI应用程序提供了重要的功能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 可解释的人工智能 (XAI)
背景情况:
- 神经网络的黑盒性质阻碍了关键领域的可靠性.
- 现有的可解释性方法往往会损害模型的性能或只提供局部解释.
研究的目的:
- 为表格数据开发基于归因的可解释分类模型.
- 为了实现高分类性能和详细模型可解释性.
主要方法:
- 将中介神经网络输出映射到可解释的数据表示空间.
- 开发基于归因的模型,选择相关特征进行分类和解释.
- 研究培训策略,以平衡性能和可解释性.
主要成果:
- 拟议的模型证明了对八个数据集的分类准确度与黑子神经网络相当.
- 该模型提供了本地和全球特征的重要性,提高了可解释性.
- 在反向精度和通用度指标上表现优于流行后期解释能力方法.
结论:
- 开发的模型提供了一个可行的解决方案,用于对表格数据进行可解释的分类.
- 突出了在模型培训过程中分类性能和可解释性之间的权衡.
- 在显著提高模型透明度的同时,实现了竞争性的准确性.
相关概念视频
Attribution Theory
13.4K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
13.4K
Classification of Systems-I
318
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
318
Fundamental Attribution Error
13.3K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.3K
Classification of Systems-II
242
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
242
Aggregates Classification
387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Classification of Signals
908
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
908


