Mining data with random forests: A survey and results of new tests

Authors:
A. Verikas;A. Gelzinis;M. Bacauskiene
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368, Ka ...;Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania;Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania
Venue:
Pattern Recognition
Year:
2011

Citing 47
Cited 13

Bayesian interpolation

Neural Computation
Bagging predictors

Machine Learning
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Foundations of statistical natural language processing

Foundations of statistical natural language processing
Soft combination of neural classifiers: a comparative study

Pattern Recognition Letters
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Complexity Measures of Supervised Classification Problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Forests

Machine Learning
Feature selection with neural networks

Pattern Recognition Letters
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Active Learning to Recognize Multiple Types of Plankton

The Journal of Machine Learning Research
Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms

Neural Computation
A Hybrid Network Intrusion Detection Technique Using Random Forests

ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
Random Forests for land cover classification

Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Rotation Forest: A New Classifier Ensemble Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
A Comparison of Decision Tree Ensemble Creation Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to classify e-mail

Information Sciences: an International Journal
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Expert Systems with Applications: An International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Diagnosing scrapie in sheep: A classification experiment

Computers in Biology and Medicine
Using Random Forests for Handwritten Digit Recognition

ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Classification tree analysis using TARGET

Computational Statistics & Data Analysis
Empirical characterization of random forest variable importance measures

Computational Statistics & Data Analysis
Predictor output sensitivity and feature similarity-based feature selection

Fuzzy Sets and Systems
The evidence framework applied to classification networks

Neural Computation
Bayesian Additive Regression Trees-Based Spam Detection for Enhanced Email Privacy

ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Ensemble of One-Class Classifiers for Network Intrusion Detection System

IAS '08 Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security
Consistency of Random Forests and Other Averaging Classifiers

The Journal of Machine Learning Research
Transaction aggregation as a strategy for credit card fraud detection

Data Mining and Knowledge Discovery
Multivariate exponential survival trees and their application to tooth prognosis

Computational Statistics & Data Analysis
Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers

Expert Systems with Applications: An International Journal
Empirical analysis of support vector machine ensemble classifiers

Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests

Expert Systems with Applications: An International Journal
Probabilistic classifiers and automated cancer registration: An exploratory application

Journal of Biomedical Informatics
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine

Computers in Biology and Medicine
Predicting customer retention and profitability by using random forests and regression forests techniques

Expert Systems with Applications: An International Journal
Multiple classifier systems in remote sensing: from basics to recent developments

MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Dynamic integration with random forests

ECML'06 Proceedings of the 17th European conference on Machine Learning
Segmenting highly articulated video objects with weak-prior random forests

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Random-Forests-Based Network Intrusion Detection Systems

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Recognizing plankton images from the shadow image particle profiling evaluation recorder

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Categorizing cells in phytoplankton images

GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
Phase congruency-based detection of circular objects applied to analysis of phytoplankton images

Pattern Recognition
Data-driven modeling approaches to support wastewater treatment plant operation

Environmental Modelling & Software
Random forests based monitoring of human larynx using questionnaire data

Expert Systems with Applications: An International Journal
A hybrid KMV model, random forests and rough set theory approach for credit rating

Knowledge-Based Systems
Dynamic Random Forests

Pattern Recognition Letters
A novel approach to estimate proximity in a random forest: An exploratory study

Expert Systems with Applications: An International Journal
Credit risk assessment and decision making by a fusion approach

Knowledge-Based Systems
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Tree ensembles for predicting structured outputs

Pattern Recognition
A novel approach to designing an adaptive committee applied to predicting company's future performance

Expert Systems with Applications: An International Journal
Evaluation of hyperspectral image classification using random forest and fukunaga-koontz transform

MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification

Applied Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.