Neural Network and Decision Tree Induction: A Comparison in the Domain of Classification of Sonar Signal

2008 ◽  
Author(s):  
Suresh S. Salankar ◽  
Balasaheb M. Patre
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Decision Tree Induction

SETTING UP A PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF HIGHWAY VEHICLES

2005 ◽  
Vol 05 (04) ◽  
pp. 411-423 ◽  
Author(s):  
MAJURA F. SELEKWA ◽  
VALERIAN KWIGIZILE ◽  
RENATUS N. MUSSA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Misclassification Rate ◽  
Tree Methods ◽  
Decision Tree Methods

Many neural network methods used for efficient classification of populations work only when the population is globally separable. In situ classification of highway vehicles is one of the problems with globally nonseparable populations. This paper presents a systematic procedure for setting up a probabilistic neural network that can classify the globally nonseparable population of highway vehicles. The method is based on a simple concept that any set of classifiable data can be broken down to subclasses of locally separable data. Hence, if these locally separable data can be identified, then the classification problem can be carried out in two hierarchical steps; step one classifies the data according to the local subclasses, and step two classifies the local subclasses into the global classes. The proposed approach was tested on the problem of classifying highway vehicles according to the US Federal Highway Administration standard, which is normally handled by decision tree methods that use vehicle axle information and a set of IF-THEN rules. By using a sample of 3326 vehicles, the proposed method showed improved classification results with an overall misclassification rate of only 2.9% compared to 9.7% of the decision tree methods. A similar setup can be used with different neural networks such as recurrent neural networks, but they were not tested in this study especially since the focus was for in situ applications where a high learning rate is desired.

scholarly journals Parkinson disease classification: a comparative analysis on classification techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1351
Author(s):  
Nazri Mohd Nawi ◽  
Mokhairi Makhtar ◽  
Zehan Afizah Afip ◽  
Mohd Zaki Salikon
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Tree ◽  
Neurological Disorders ◽  
Early Stage ◽  
Current Trend ◽  
Disease Classification ◽  
High Demand

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

An efficient pre-processing method for improved classification of diabetics using decision tree and artificial neural network

2019 ◽  
Author(s):  
D. Venkata Vara Prasad ◽  
Lokeswari Venkataramana ◽  
Priyanka Balasubramanian ◽  
B. Priyankha ◽  
Shrinidhi Rajagopal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Processing Method ◽  
Artificial Neural

scholarly journals Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA

2021 ◽  
Vol 9 ◽  
Author(s):  
Elakkiya R. ◽  
Deepak Kumar Jain ◽  
Ketan Kotecha ◽  
Sharnil Pandya ◽  
Sai Siddhartha Reddy ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Deep Neural Network ◽  
Imbalanced Data ◽  
Vital Role ◽  
Biological Data ◽  
Decision Tree Classifier ◽  
Genome Data ◽  
Tree Classifier

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.

Hybrid Classification in Bag of Visual Words Model

2018 ◽  
Vol 3 (4) ◽  
pp. 10-15
Author(s):  
Baldemar Zurita ◽  
Luís Luna ◽  
José Hernández ◽  
José Ramírez
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Decision Tree ◽  
Second Phase ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Hybrid Classification ◽  
Classification Of Images ◽  
Training Sets

Classification of images by means of the BOVW method is well known and applied in different recognition projects, this method rely on three phases: detection and extraction of characteristics, representation of the image and finally the classification. SIFT, Kmeans and SVM is the most accepted combination. This article aims to demonstrate that this combination is not always the best choice for all types of datasets, different training sets of images were created from scratch and will be used for the bag of visual words model: the first phase of detection and extraction, SIFT will be used, later in the second phase a dictionary of words will be created through a clustering process using K-means, EM, K-means in combination with EM, finally, for classification it will be compared the algorithms of SVM, Gaussian NB, KNN, Decision Tree, Random Forest, Neural Network and AdaBoost in order to determine the performance and accuracy of every method.

scholarly journals Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chenxi Yang ◽  
Banish D. Ojha ◽  
Nicole D. Aranoff ◽  
Philip Green ◽  
Negar Tavassolian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Aortic Stenosis ◽  
Decision Tree ◽  
Transfer Learning ◽  
Mechanical Signals ◽  
Extreme Gradient Boosting

Abstract This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

scholarly journals INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS

2021 ◽  
Vol 3 (2) ◽  
pp. 93-100
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Negative Consequences ◽  
Accuracy Rate ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Methods ◽  
Network Method ◽  
Cyberbullying Detection

Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study

Sign in / Sign up

Export Citation Format

Share Document