可解释性
分类器(UML)
人工智能
多类分类
计算机科学
机器学习
随机子空间法
模式识别(心理学)
人工神经网络
支持向量机
作者
Abeegithan Jeyasothy,Suresh Sundaram,Savitha Ramasamy,N. Sundararajan
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:54 (1): 3-12
被引量:1
标识
DOI:10.1109/tcyb.2022.3181181
摘要
This article presents a new approach for providing an interpretation for a spiking neural network classifier by transforming it to a multiclass additive model. The spiking classifier is a multiclass synaptic efficacy function-based leaky-integrate-fire neuron (Mc-SEFRON) classifier. As a first step, the SEFRON classifier for binary classification is extended to handle multiclass classification problems. Next, a new method is presented to transform the temporally distributed weights in a fully trained Mc-SEFRON classifier to shape functions in the feature space. A composite of these shape functions results in an interpretable classifier, namely, a directly interpretable multiclass additive model (DIMA). The interpretations of DIMA are also demonstrated using the multiclass Iris dataset. Further, the performances of both the Mc-SEFRON and DIMA classifiers are evaluated on ten benchmark datasets from the UCI machine learning repository and compared with the other state-of-the-art spiking neural classifiers. The performance study results show that Mc-SEFRON produces similar or better performances than other spiking neural classifiers with an added benefit of interpretability through DIMA. Furthermore, the minor differences in accuracies between Mc-SEFRON and DIMA indicate the reliability of the DIMA classifier. Finally, the Mc-SEFRON and DIMA are tested on three real-world credit scoring problems, and their performances are compared with state-of-the-art results using machine learning methods. The results clearly indicate that DIMA improves the classification accuracy by up to 12% over other interpretable classifiers indicating a better quality of interpretations on the highly imbalanced credit scoring datasets.
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