Deep Fuzzy Rule-Based Classification System With Improved Wang–Mendel Method

可解释性 人工智能 模糊逻辑 模糊规则 计算机科学 模糊控制系统 维数之咒 神经模糊 模糊分类 机器学习 深度学习 图层(电子) 数据挖掘 模式识别(心理学) 化学 有机化学
作者
Yuangang Wang,Haoran Liu,Wenhao Jia,Shuo Guan,Xiaodong Liu,Xiaodong Duan
出处
期刊:IEEE Transactions on Fuzzy Systems [Institute of Electrical and Electronics Engineers]
卷期号:30 (8): 2957-2970 被引量:6
标识
DOI:10.1109/tfuzz.2021.3098339
摘要

Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular data classification problem. However, original WM fuzzy system is weak in handling dataset with high dimensionality or large volume. Meanwhile, its capability of characterizing data is narrow, which results from lacking hierarchical transformation of features like deep learning-based models. In this article, we propose a deep fuzzy rule-based classification system (DFRBCS) based on improved WM method, in which fuzzy technique and deep learning strategy are combined to make a desirable tradeoff between model’s interpretability and prediction accuracy. We first redefine the consequent part of fuzzy rule in WM fuzzy system with class probability vector, which endows the improved WM fuzzy system with capacity for serving as building block of deep model. The model structure of DFRBCS is designed in layer-by-layer manner, where raw features can be transformed hierarchically. For every layer in DFRBCS, it contains many improved WM fuzzy systems whose input spaces are generated by shuffling and sliding window operation on concatenated outputs of fuzzy systems in previous layer. Comparative experiments are conducted on 45 real-world datasets with various sizes and dimensionality between our method, five baseline models, and the other deep fuzzy classifiers (D-TSK-FC, HID-TSK-FC, FCCI-TSK, DSA-FC, and MEEFIS). The experimental results show that DFRBCS is competitive in classification performance and promising in model’s interpretability.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
福尔摩云发布了新的文献求助30
刚刚
niunai完成签到 ,获得积分10
1秒前
JY发布了新的文献求助10
1秒前
mmddlj完成签到 ,获得积分10
1秒前
byzhao19完成签到,获得积分10
2秒前
ting发布了新的文献求助10
3秒前
whatever应助能干的吐司采纳,获得10
3秒前
4秒前
思源应助w2503采纳,获得10
4秒前
霸气皮皮虾完成签到,获得积分20
4秒前
FashionBoy应助Young采纳,获得10
4秒前
5秒前
5秒前
Ava应助不知名网友要某某采纳,获得10
5秒前
5秒前
乐乐应助虚心的芹采纳,获得20
5秒前
鳗鱼匕发布了新的文献求助10
5秒前
汉堡包应助bing采纳,获得10
6秒前
6秒前
风趣的寻凝完成签到,获得积分10
6秒前
ccccchen完成签到,获得积分10
6秒前
6秒前
Dr.c完成签到,获得积分10
6秒前
6秒前
adam完成签到,获得积分10
6秒前
善学以致用应助wendinfgmei采纳,获得10
7秒前
janie完成签到,获得积分10
7秒前
zimablue完成签到,获得积分10
7秒前
谭谨川完成签到,获得积分10
7秒前
7秒前
7秒前
you完成签到,获得积分10
7秒前
段盈完成签到,获得积分10
7秒前
小熊完成签到,获得积分10
8秒前
善学以致用应助May采纳,获得10
8秒前
沉默沛岚发布了新的文献求助10
8秒前
8秒前
肉松完成签到,获得积分10
8秒前
hafahafa发布了新的文献求助10
9秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970949
求助须知:如何正确求助?哪些是违规求助? 3515634
关于积分的说明 11179061
捐赠科研通 3250769
什么是DOI,文献DOI怎么找? 1795474
邀请新用户注册赠送积分活动 875831
科研通“疑难数据库(出版商)”最低求助积分说明 805188