Support Vector Machine Decision Trees with Rare Event Detection

Abstract Logistic regression is a popular and powerful classification method. The addition of ridge regularization and optimization using a combination of linear conjugate gradients and IRLS, called Truncated Regularized Iteratively Re-weighted Least Square (TR-IRLS), can outperform Support Vector Machine (SVM) in terms of processing speed, especially when applied to large data and have competitive accuracy. However, neither SVM nor TR-IRLS is good enough when applied to unbalanced data. Fuzzy Support Vector Machine (FSVM) is an SVM development for unbalanced data that adds fuzzy membership to each observation. The fuzzy membership makes the interest of each observation in the minority class higher than the majority class. Meanwhile, TR-IRLS developed into a Rare Event Weighted Logistic Regression (RE-WLR) by adding weight to logistic regression and bias correction. The weighting of the RE-WLR depends on the undersampling scheme. It allows an “information loss”. Between FSVM and RE-WLR has a similarity, the weight based only on class differences (minority or majority). Entropy Based Fuzzy Support Vector Machine (EFSVM) is a method used to accommodate the weaknesses of FSVM by considering the class certainty of class observations. As a result, EFSVM is able to improve SVM performance for unbalanced data, even beating FSVM. For this reason, we use EF on the TR-IRLS algorithm to classify large and unbalanced data, as a proposed method. This method is called Entropy-Based Fuzzy Weighted Logistic Regression (EF-WLR). This Research shows the review of EF-WLR for unbalanced data classification.

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Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

Journal of Modeling and Simulation of Materials ◽

10.21467/jmsm.4.1.7-18 ◽

2021 ◽

Vol 4 (1) ◽

pp. 7-18

Author(s):

Donata D Acula

Keyword(s):

Support Vector Machine ◽

Decision Trees ◽

Ensemble Methods ◽

The Philippines ◽

Support Vector ◽

Adaptive Boosting ◽

Risk Levels ◽

Disaster Data ◽

Boosting Algorithm ◽

Intelligent Approach

This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.

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Event detection in sports video based on audio-visual and support vector machine. Case-study: football

International Journal of Internet Technology and Secured Transactions ◽

10.1504/ijitst.2019.10019549 ◽

2019 ◽

Vol 9 (1/2) ◽

pp. 26

Author(s):

Vijayan Ellapan ◽

R. Rajkumar

Keyword(s):

Support Vector Machine ◽

Event Detection ◽

Support Vector ◽

Sports Video

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Support Vector Machine Pre-pruning Approaches on Decision Trees for Better Classification

Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology ◽

10.1145/3362752.3362763 ◽

2019 ◽

Author(s):

Doreen Ying Ying Sim

Keyword(s):

Support Vector Machine ◽

Decision Trees ◽

Support Vector

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DMiner-I: A software tool of data mining and its applications

Robotica ◽

10.1017/s0263574702004307 ◽

2002 ◽

Vol 20 (5) ◽

pp. 499-508

Author(s):

Jie Yang ◽

Chenzhou Ye ◽

Nianyi Chen

Keyword(s):

Neural Network ◽

Data Mining ◽

Genetic Algorithm ◽

Pattern Recognition ◽

Support Vector Machine ◽

Knowledge Representation ◽

Decision Trees ◽

Software Tool ◽

Support Vector ◽

Function Models

SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

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