Random Forest (RF) is an ensemble classication technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance in terms of predictive accuracy. This explains why, over the past decade, there have been many extensions of RF where each extension employed a variety of techniques and strategies to improve certain aspect(s) of RF. Since it has been proven empirically that ensembles tend to yield better results when there is a signicant diversity among the constituent models, the objective of this paper is twofolds. First, it investigates how an unsupervised learning technique, namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the RF. Second, trees with the highest LOF scores are then used to produce an extension of RF termed LOFB-DRF that is much smaller in size than RF, and yet performs at least as good as RF, but mostly exhibits higher performance in terms of accuracy. The latter refers to a known technique called ensemble pruning. Experimental results on 10 real datasets prove the superiority of our proposed extension over the traditional RF. Unprecedented pruning levels reaching as high as 99 have been achieved at the time of boosting the predictive accuracy of the ensemble. The notably high pruning level makes the technique a good candidate for real-time applications.
@cite_30: Abstract In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of ‘error-independent’ networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.
@cite_35: Diversity among the base classifiers is deemed to be important when constructing a classifier ensemble. Numerous algorithms have been proposed to construct a good classifier ensemble by seeking both the accuracy of the base classifiers and the diversity among them. However, there is no generally accepted definition of diversity, and measuring the diversity explicitly is very difficult. Although researchers have designed several experimental studies to compare different diversity measures, usually confusing results were observed. In this paper, we present a theoretical analysis on six existing diversity measures (namely disagreement measure, double fault measure, KW variance, inter-rater agreement, generalized diversity and measure of difficulty), show underlying relationships between them, and relate them to the concept of margin, which is more explicitly related to the success of ensemble learning algorithms. We illustrate why confusing experimental results were observed and show that the discussed diversity measures are naturally ineffective. Our analysis provides a deeper understanding of the concept of diversity, and hence can help design better ensemble learning algorithms.
@cite_9: Abstract The topic of this paper is the exploitation of diversity to enhance computer system reliability. It is well established that a diverse system composed of multiple alternative versions is more reliable than any single version alone, and this knowledge has occasionally been exploited in safety-critical applications. However, it is not clear what this property is, nor how the available diversity in a collection of versions is best exploited. We develop, define, illustrate and assess diversity measures, voting strategies for diversity exploitation, and interactions between the two. We take the view that a proper understanding of such issues is required if multiversion software engineering is to be elevated from the current “try it and see” procedure to a systematic technology. In addition, we introduce inductive programming techniques, particularly neural computing, as a cost-effective route to the practical use of multiversion systems outside the demanding requirements of safety-critical systems, i.e. in general software engineering.
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@cite_5: Preface.Preface to the Second Edition.Preface to the First Edition.1. An Introduction to Applied Probability.2. Statistical Inference for a Single Proportion.3. Assessing Significance in a Fourfold Table.4. Determining Sample Sizes Needed to Detect a Difference Between Two Proportions.5. How to Randomize.6. Comparative Studies: Cross-Sectional, Naturalistic, or Multinomial Sampling.7. Comparative Studies: Prospective and Retrospective Sampling.8. Randomized Controlled Trials.9. The Comparison of Proportions from Several Independent Samples.10. Combining Evidence from Fourfold Tables.11. Logistic Regression.12. Poisson Regression.13. Analysis of Data from Matched Samples.14. Regression Models for Matched Samples.15. Analysis of Correlated Binary Data.16. Missing Data.17. Misclassification Errors: Effects, Control, and Adjustment.18. The Measurement of Interrater Agreement.19. The Standardization of Rates.Appendix A. Numerical Tables.Appendix B. The Basic Theory of Maximum Likelihood Estimation.Appendix C. Answers to Selected Problems.Author Index.Subject Index.
Regardless of the diversity creation technique used, diversity measures were developed to measure the diversity of a certain technique or perhaps to compare the diversity of two techniques. @cite_35 presented a theoretical analysis on six existing diversity measures: disagreement measure @cite_21 , double fault measure @cite_30 , KW variance @cite_6 , inter-rater agreement @cite_5 , generalized diversity @cite_9 , and measure of difficulty @cite_5 . The goal was not only to show the underlying relationships between them, but also to relate them to the concept of margin, which is one of the contributing factors to the success of ensemble learning algorithms.