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

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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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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Performance analysis of fuzzy techniques hierarchical aggregation functions decision trees and Support Vector Machine (SVM)for the classification of epilepsy risk levels from EEG signals

2011 IEEE Recent Advances in Intelligent Computational Systems ◽

10.1109/raics.2011.6069364 ◽

2011 ◽

Author(s):

R. Harikumar ◽

T. Vijaykumar ◽

C. Palanisamy

Keyword(s):

Support Vector Machine ◽

Performance Analysis ◽

Decision Trees ◽

Support Vector ◽

Eeg Signals ◽

Risk Levels ◽

Hierarchical Aggregation ◽

Aggregation Functions ◽

Fuzzy Techniques

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Sarcasm Detection Using Different Machine Learning Algorithms

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1594 ◽

2021 ◽

pp. 1-5

Author(s):

Harshal Surve ◽

Aditya Mestry

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Trees ◽

Naive Bayes ◽

Learning Algorithms ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

Logistics Regression ◽

Sentence Meaning

Sarcasm is the use of words usually used to indirectly either mock or annoy someone, or for humorous purposes. One of the difficult modes of communication for machines to identify is sarcasm. People often use sarcasm in their daily communication to indirectly annoy people which makes it very important to identify the sentence meaning. There are various machine learning algorithms for sarcasm detection such as Naïve Bayes (NB), Support Vector Machine (SVM), Logistics Regression (LR), Decision Trees (DT).All these algorithm can be used for Sarcasm Detection. The main goal of this paper is to provide various machine learning algorithms for sarcasm detection.

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