%0 Generic %1 liu2020dendrite %A Liu, Gang %A Wang, Jing %D 2020 %K Classification Machine_learning Regression algorithms engineering pattern_recognition %T Dendrite Net: A White-Box Module for Classification, Regression, and System Identification %U http://arxiv.org/abs/2004.03955 %X This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs ($ and or not $). Experiments and results: DD, the first white-box machine learning algorithm, showed excellent system identification performance for the black-box system. Secondly, it was verified by nine real-world applications that DD brought better generalization capability relative to MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Thirdly, by MINIST and FASHION-MINIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than Cell body Net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids over-fitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in $ MATLAB $ and $ PyTorch $ ($ Python $) demonstrated that DD was faster than Cell body Net both in epoch and forward-propagation. We highlight DD's white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at https://github.com/liugang1234567/Gang-neuron.

@preprint{liu2020dendrite, abstract = {This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs ($ and \backslash or \backslash not $). Experiments and results: DD, the first white-box machine learning algorithm, showed excellent system identification performance for the black-box system. Secondly, it was verified by nine real-world applications that DD brought better generalization capability relative to MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Thirdly, by MINIST and FASHION-MINIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than Cell body Net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids over-fitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in $ MATLAB $ and $ PyTorch $ ($ Python $) demonstrated that DD was faster than Cell body Net both in epoch and forward-propagation. We highlight DD's white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at https://github.com/liugang1234567/Gang-neuron.}, added-at = {2020-09-29T19:41:26.000+0200}, author = {Liu, Gang and Wang, Jing}, biburl = {https://www.bibsonomy.org/bibtex/2a3b3a8449a26c1079f7ce8bd780e78d3/gang_m_liu}, description = {Dendrite Net: A White-Box Module for Classification, Regression, and System Identification}, interhash = {311649f3c9a4e5bca78ead98def99a76}, intrahash = {a3b3a8449a26c1079f7ce8bd780e78d3}, keywords = {Classification Machine_learning Regression algorithms engineering pattern_recognition}, note = {cite arxiv:2004.03955Comment: Submitted to TPAMI. Renamed DD for avoiding confusion}, timestamp = {2020-09-29T19:45:40.000+0200}, title = {Dendrite Net: A White-Box Module for Classification, Regression, and System Identification}, url = {http://arxiv.org/abs/2004.03955}, year = 2020 }