Book ChapterDOI
New machine learning algorithm: random forest
Yanli Liu,Yourong Wang,Jian Zhang +2 more
- pp 246-252
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TLDR
This Paper gives an introduction of Random Forest, a new Machine Learning Algorithm and a new combination Algorithm that has been wildly used in classification and prediction, and used in regression too.Abstract:
This Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. Random Forest has been wildly used in classification and prediction, and used in regression too. Compared with the traditional algorithms Random Forest has many good virtues. Therefore the scope of application of Random Forest is very extensive.read more
Citations
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Proceedings ArticleDOI
A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks
TL;DR: In this article , a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset, the model contains two function, in the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous.
Journal ArticleDOI
Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey
TL;DR: In this paper , the authors investigated the professions of the future and current in Turkey by the application of supervised learning algorithms and clustering methods to various Turkish data including documents belonging to Turkey's institutions.
Journal ArticleDOI
Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique
TL;DR: In this article , the authors presented the results of the classification of diffuse reflectance (DR) spectra and multiexcitation autofluorescence spectra that were collected in vivo from precancerous and benign skin lesions at three different source detector separation (SDS) values.
Book ChapterDOI
An Approach for Detecting Gaming the System Behavior in Programming Problem-Solving
TL;DR: In this article , the authors explored the phenomenon of gaming the system behavior, studying underlying factors related to automatically detecting when students game the system and developed a model for detecting gaming behavior in a computer programming student during problem-solving activities.
Book ChapterDOI
Ensemble Machine Learning Models for Breast Cancer Identification
TL;DR: In this paper , the authors focused on ensemble ML models after using the synthetic minority oversampling technique (SMOTE) with 10-fold cross-validation, and compared the models in terms of precision, accuracy, recall and area under the curve (AUC).
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Journal ArticleDOI
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.