An analysis of diversity measures

By E. Ke Tang, Ponnuthurai Nagaratnam Suganthan, Xin Yao. Machine-mediated learning, 2006

436 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/82c291ff9f97931e6a2806fc082f82e1d792de49

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Citations in the corpus

Towards Making Unlabeled Data Never Hurt (2011, background): “rather than necessary condition for safeness, and ii) the analysis of the diversity among low-density separators [39], provides an explanation to S4VMs’ superiority to single separator.”
Practical Ensemble Classification Error Bounds for Different Operating Points (2013, background): “In either case, the tradeoff between the probabilities of false alarm and missed detection is important to take into account.”
Stochastic Classifiers for Unsupervised Domain Adaptation (2020, background): “Recently, several studies utilised the concept of two diverse classifiers [53] in ensemble learning for unsupervised domain adaptation.”
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests (2011, methodology): “The margin mi of instance xi is computed as follows (Tang et al., 2006): mi = margin(xi, lj) = vi,lj − ∑ c≠lj vi,c∑ c vi,c (5) where vi,lj is the number of votes for the true class lj, and vi,c is the number of votes for any class c with c ≠ lj.”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, background): “However, it is shown in [40] that the relationship between”
DDD: A New Ensemble Approach for Dealing with Concept Drift (2012, background): “3.2 Ensemble Diversity in the Presence of Drifts Even though several studies of ensemble diversity can be found in the literature, e.g., [28], [29], [30], the study presented in [9] is the first diversity analysis regarding online learning in the presence of concept drift.”
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning (2016, background): “Entropy is the bestknown measure of diversity in statistical research, but there are many others (Tang et al., 2006; Gimpel et al., 2013).”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, background): “Diversity is a key to ensemble methods and the importance of diversity was explained in [1], [28].”
Using Class Imbalance Learning for Software Defect Prediction (2013, background): “Ensemble learning algorithms have been shown to be able to combine strength from individual learners, and enhance the overall performance [25], [26].”
A survey of multiple classifier systems as hybrid systems (2014, background): “The analysis of several diversity measures [70] relating them to the concept of classifiers’ margin, showed their limitations and the source of confusing empirical results.”
Random Forests for Big Data (2015, background): “This scheme can be extended by using other diversity measures, see [34] presenting a theoretical analysis on six existing diversity measures.”
Rough Sets: Selected Methods and Applications in Management and Engineering (2012, background): “[57], Golan and Edwards [58, 59] applied the rough sets model to discover strong trading rules, which reflect highly repetitive patterns in data, from historical database of the Toronto stock exchange.”