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Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree

Wenhao Xie, Yanhong She, Qiao Guo, Michele Risi
2021 Scientific Programming  
In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm.  ...  The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.  ...  Conclusion In this paper, an improved multiclassification algorithm based on the balanced binary decision tree, called IBDT-SVM algorithm, is proposed. is algorithm improves the original BDT-SVM algorithm's  ... 
doi:10.1155/2021/5560465 fatcat:matcbz5rfnha5idlz3ys5jcwgy

LSTSVM-PBT Multi-class Classification

Qing Yu, Lihui Wang
2017 DEStech Transactions on Engineering and Technology Research  
SVM is used to solve the problem of the binary-class classification.  ...  basic category of classifier for multi-class SVM in this paper.  ...  least square twin SVM partial binary tree proposed in this paper has improved significantly on time performance compares with other multiple-class classification algorithm (OVO -SVM and OVA -SVM ), the  ... 
doi:10.12783/dtetr/mimece2016/10023 fatcat:4q3tnisuy5c4jgxabouu6d5niy

Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine

Patrick Jansen, Michael Vollnhals, Daniel Renner, David Vergossen, Werner John, Jürgen Götze
2016 Advances in Radio Science  
</strong> Further improvements on the novel method for state of charge (SOC) determination of lithium iron phosphate (LFP) cells based on the impedance spectra classification are presented.  ...  As a SVM is a binary classifier, only the distinction between two SOC can be computed in one iteration of the algorithm. Therefore a search pattern is necessary.  ...  The responsibility for this publication is held by the authors only. Edited by: J. Anders Reviewed by: two anonymous referees  ... 
doi:10.5194/ars-14-55-2016 fatcat:vn27fbyzyzb6pijhchi62j5ciy

Advance Probabilistic Binary Decision Tree using SVM

Anita Meshram, Roopam Gupta, Sanjeev Sharma
2014 International Journal of Computer Applications  
In the testing phase, the algorithm depends on rooted binary directed acyclic graph to make a decision. So the classification of DAG is usually faster than OaO.  ...  APBDT-SVM combines Binary Decision Tree (BDT) and Probabilistic SVM is an effective way for solving multiclass problem.  ...  [10] present an improved version of One-against-All(OAA) method for multiclass SVM classification based on decision tree.  ... 
doi:10.5120/18956-0256 fatcat:rmgsnihrm5ct5ffh5o6fso4ssq

Comparison of Predictive Models for Transferring Stroke In-Patients to Intensive Care Unit

Nawal N. Alotaibi, Sreela Sasi
2016 Transactions on Machine Learning and Artificial Intelligence  
In this research, initially, a Decision Tree (DT) model, an Artificial Neural Network (ANN) model, a Support Vector Machine (SVM) model, and a Logistic Regression (LR) model are evaluated for predicting  ...  A DT model, an ANN model, a SVM model, and a LR model are evaluated again on the balanced dataset for prediction.  ...  ACKNOWLEDGMENT The authors would like to express their deepest gratitude to the National Neuroscience Institute at King Fahad Medical City in Riyadh, Saudi Arabia for providing the dataset for this research  ... 
doi:10.14738/tmlai.43.2051 fatcat:amiktav5pnf4lexld2c2mnpsve

The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease – A Machine Learning-Based Fusion Approach [article]

Hafsa Binte Kibria, Abdul Matin
2022 arXiv   pre-print
Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease.  ...  The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary.  ...  In our model, C was 1 and 10 respectively for binary and multi-class. Decision tree(DT) A decision tree is a tree-like arrangement that is built based on attributes.  ... 
arXiv:2203.04921v1 fatcat:ae6zotbr2vcovp5o3t6k6vxrde

DCSVM: Fast Multi-class Classification using Support Vector Machines [article]

Duleep Rathgamage Don, Ionut E. Iacob
2018 arXiv   pre-print
We present DCSVM, an efficient algorithm for multi-class classification using Support Vector Machines.  ...  The algorithm continues recursively, reducing the number of classes at each step, until a final binary decision is made between the last two classes left in the competition.  ...  However, SVM is primarily a binary classification tool. The multiclass classification with SVM is still an ongoing research problem (see, for example, [3, 22, 17, 18] for some recent work).  ... 
arXiv:1810.09828v1 fatcat:srqlfte73jbkpbae46lt6hehfe

Machine Learning: A Review on Binary Classification

Roshan Kumari, Saurabh Kr.
2017 International Journal of Computer Applications  
This research synthesizes binary classification in which various approaches for binary classification are discussed.  ...  Sockpuppet detection is based on binary, in which given accounts are detected either sockpuppet or non-sockpuppet.  ...  Multiple identity is also an example of binary classification, in which one person has more than one account for malicious use.  ... 
doi:10.5120/ijca2017913083 fatcat:h4iwibashzhuzazhilnmc3xloi

Extensive Huffman-tree-based Neural Network for the Imbalanced Dataset and Its Application in Accent Recognition

Jeremy Merrill, Yu Liang, Dalei Wu
2021 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)  
classifiers (e.g., CNN or SVM) using an extensive Huffman tree.  ...  Compared with the Binary Huffman-Tree Neural Network (BHTNN), an EHTNN may have a smaller tree height, involve fewer component neural networks, and demonstrate more flexibility on handling data imbalance  ...  However, due to the computational limitations of SVM-based neural networks for data with multiple features, we are proposing a CNN-based Binary Huffman Tree Neural Network.  ... 
doi:10.1109/icaiic51459.2021.9415243 fatcat:42t6qskxczhkzg7nno7gqrrjai

Vote-Based: Ensemble Approach

Abdul Ahad ABRO
2021 Sakarya University Journal of Science  
This method can be used for binary classification, image recognition and machine learning problems. than using a single model.  ...  Vote-based is one of the ensembles learning methods in which the individual classifier is situated on numerous weighted categories of the training datasets.  ...  The main idea of this research is summarized in the following manner: I) Vote-based ensemble learning method improves the binary classification performance accuracy.  ... 
doi:10.16984/saufenbilder.901960 fatcat:d3jbmx4yybh7lossvblmfid6ja

Binary Classication Tree for Multiclass Classication with Observation-based Clustering

Maythapolnun Athimethphat, Boontarika Lerteerawong
1970 ECTI Transactions on Computer and Information Technology  
The experiment shows how our proposed algorithm (BCTOB) performed on different data sets, compared with other binary classification tree algorithms.  ...  Unlike a traditional class-clustering approach, this observation-based algorithm allows one class to appear in two meta-classes so it can be examined in both sub-trees.  ...  A binary classification tree like Half-Against-Half [2] , Divide-by-2 [3] , and SVM Binary Decision Tree [4] uses different unsupervised clustering techniques on class centroids to group classes into  ... 
doi:10.37936/ecti-cit.201262.54335 fatcat:33z5hgz25zhj3mfi22ikzvx3le

A Comparative Analysis of Android Malware [article]

Neeraj Chavan, Fabio Di Troia, Mark Stamp
2019 arXiv   pre-print
Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model  ...  In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features.  ...  Then, a voting procedure based on these multiple decision trees is typically used to determine the random forest classification. In each of our random forest experiments, we use 100 trees.  ... 
arXiv:1904.00735v1 fatcat:7yrpdklny5dzxm4vxz3z6qt2y4

Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications

Yiyan Zhang, Yi Xin, Qin Li, Jianshe Ma, Shuai Li, Xiaodan Lv, Weiqi Lv
2017 BioMedical Engineering OnLine  
K-nearest neighbor and C4.5 decision tree algorithms perform well on binary-and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task.  ...  Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly.  ...  Consent for publication Not applicable. Ethics approval and consent to participate Not applicable.  ... 
doi:10.1186/s12938-017-0416-x pmid:29096638 pmcid:PMC5668968 fatcat:chqj7xyifvctzpbz643mgoipwa

Accounts Receivable Seamless Prediction for Companies by Using Multiclass Data Mining Model

Ferry Irawan, Febriliyan Samopa
2019 IPTEK Journal of Proceedings Series  
Based on previous studies, research has been conducted to develop a data mining model to produce the best classification model to predict a customer's payment capabilities.  ...  With the application of data mining approaches using oversampling, feature selection (FS) algorithm, including Relief, Information Gain Ratio, PCA, and multiclass algorithm, including Random Forest, SVM  ...  Decision tree is the most commonly used algorithm by researchers in scoring credit scoring.  ... 
doi:10.12962/j23546026.y2019i1.5096 fatcat:goetzw2pczdezansgv3jrpkyha

An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-based Enhanced Stacking Multiple Binary Classifiers

AbdAllah A. AlHabshy, Bashar I. Hameed, Kamal A. ElDahshan
2022 IEEE Access  
The EMBAM ensemble multiple binary classifiers into a single model by stacking. The core binary model is a decision tree classifier with hyperparameters optimized using the grid search method.  ...  This massive data volume may be used to provide high-value information for decision support, forecasting, business intelligence, data-intensive science research, etc.  ...  The decision tree binary classifier has superiority as the base binary model for the EMBAM.  ... 
doi:10.1109/access.2022.3174482 fatcat:xfbsqwyxarhzfnhwaq7hv3rubq
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