scholarly journals Automatic Induction of Neural Network Decision Tree Algorithms

2019 ◽  
pp. 697-704 ◽  
Author(s):  
Chapman Siu
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Tree Algorithms

scholarly journals Near-Optimal Sparse Neural Trees for Supervised Learning

2021 ◽  
Author(s):  
Tanujit Chakraborty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Tree ◽  
Mathematical Formulation ◽  
Machine Learning Algorithms ◽  
Optimal Decision ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Tree Algorithms

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

scholarly journals Near-optimal Sparse Neural Trees

2021 ◽  
Author(s):  
Tanujit Chakraborty ◽  
Tanmoy Chakraborty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Classification Tree ◽  
Mathematical Formulation ◽  
Machine Learning Algorithms ◽  
Optimal Decision ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Tree Algorithms

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal upto some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India

2013 ◽  
Vol 68 (12) ◽  
pp. 2521-2526 ◽  
Author(s):  
A. R. Senthil kumar ◽  
Manish Kumar Goyal ◽  
C. S. P. Ojha ◽  
R. D. Singh ◽  
P. K. Swamee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Correlation Function ◽  
Decision Tree ◽  
Soft Computing ◽  
Auto Correlation ◽  
Soft Computing Techniques ◽  
Tree Algorithms ◽  
Persistence Model

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

scholarly journals Diagnosis of Long Sightedness Using Neural Network and Decision Tree Algorithms

2021 ◽  
Vol 1767 (1) ◽  
pp. 012021
Author(s):  
Roseline Oluwaseun Ogundokun ◽  
Peter O. Sadiku ◽  
Sanjay Misra ◽  
Opeyemi Eyitayo Ogundokun ◽  
Joseph Bamidele Awotunde ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Tree Algorithms

scholarly journals COLOR RECOGNITION WEARABLE DEVICE USING MACHINE LEARNING FOR VISUALY IMPAIRED PERSON

2018 ◽  
Vol 19 (2) ◽  
pp. 213-220
Author(s):  
NIK NUR WAHIDAH NIK HASHIM ◽  
TAREK MOHAMED BOLAD ◽  
Noor Hazrin Hany Mohamad Hanif
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Decision Tree ◽  
Open Source ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Tree Algorithms ◽  
Impaired Person ◽  
Color Conversion

ABSTRACT: Recognizing colors is a concerning problem for the visually impaired person. The aim of this paper is to convert colors to sound and vibration in order to allow fully/partially blind people to have a ‘feeling’ or better understanding of the different colors around them. The idea is to develop a device that can produce vibration for colors. The user can also hear the name of the color along with ‘feeling’ the vibration. Two algorithms were used to distinguish between colors;  RGB to HSV color conversion in comparison with neural network and decision tree based machine learning algorithms. Raspberry Pi 3 with Open Source Computer Vision (OpenCV) software handles the image processing. The results for RGB to HSV color conversion algorithm were performed with 3 different colors (red, blue, and green). In addition, neural network and decision tree algorithms were trained and tested with eight colors (red, green, blue, orange, yellow, purple, white, and black) for the conversion to sound and vibration. Neural network and decision tree algorithms achieved higher accuracy and efficiency for the majority of tested colors as compared to the RGB to HSV. ABSTRAK: Membezakan antara warna adalah masalah yang merunsingkan terutamanya kepada mereka yang buta, separa buta atau buta warna. Tujuan kertas penyelidikan ini adalah untuk membentangkan kaedah menukar warna kepada bunyi dan getaran bagi membolehkan individu yang buta, separa buta atau buta warna untuk mendapat ‘perasaan’ atau pemahaman yang lebih baik tentang warna-warna yang berbeza disekeliling mereka. Idea yang dicadangkan adalah dengan membuat sebuah alat yang dapat menghasilkan getaran bagi setiap warna yang berbeza. Disamping itu, pengguna juga dapat mendengar nama warna tersebut. Algoritma yang digunakan untuk membezakan antara warna adalah penukaran warna RGB kepada HSV yang dibandingkan dengan rangkaian neural dan algoritma pembelajaran mesin berasaskan pokok keputusan. Raspberry Pi 3 bersaiz kad kredit dengan perisian Open Source Computer Vision (OpenCV) mengendalikan pemprosesan imej. Hasil algoritma penukaran warna RGB kepada HSV telah dilakukan dengan tiga warna yang berbeza (merah, biru, dan hijau). Tambahan pula, hasil rangkaian neural dan algoritma berasaskan pokok keputusan telah dilakukan dengan lapan warna (merah, hijau, biru, oren, kuning, ungu, putih, dan hitam) dengan penukaran warna tersebut kepada bunyi dan getaran. Selain itu, hasil rangkaian neural dan algoritma berasaskan pokok keputusan mencapai hasil dapatan yang baik dengan ketepatan dan kecekapan yang tinggi bagi kebanyakan warna yang diuji berbanding RGB kepada HSV.

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