Probabilistic Confusion Entropy for Evaluating Classifiers

Wang, Xiaoning; Wei, Jinmao; Han, Jin; Yu, Gang; Zhang, Haiwei

doi:10.3390/e15114969

Cited by 21 publications

(14 citation statements)

References 44 publications

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“…Real To analyze the probability sensitivity of the GP in our bankruptcy prediction context, we compute a probabilistic confusion entropy matrix based on the model estimated probabilities, following a similar approach of [56]. For our matrix, we replaced the usual values of true/false positives/negatives by their corresponding averaged probabilities at each test point, as summarized in Table 7.…”

Section: Datasetmentioning

confidence: 99%

Probabilistic modeling and visualization for bankruptcy prediction

Antunes

Ribeiro

Pereira

2017

Applied Soft Computing

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In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian processes (GP) in the context of bankruptcy prediction, comparing it against the support vector machines (SVM) and the logistic regression (LR). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks under extremely competitive and volatile business environments.

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Section: Datasetmentioning

confidence: 99%

Probabilistic modeling and visualization for bankruptcy prediction

Antunes

Ribeiro

Pereira

2017

Applied Soft Computing

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“…Finally, in Equation (1), let us define H the global entropy of the collaborative system for all algorithms. This measure based on the probabilistic confusion entropy [27] is used as an entropy measure between the different solutions and as a stopping criterion for the EBCC algorithm.…”

Section: Given J Views Let Us Considermentioning

confidence: 99%

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Sublime

Cabanès

Matei

2019

Entropy

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The aim of collaborative clustering is to enhance the performances of clustering algorithms by enabling them to work together and exchange their information to tackle difficult data sets. The fundamental concept of collaboration is that clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. This kind of collaborative learning can be beneficial to a wide number of tasks including multi-view clustering, clustering of distributed data with privacy constraints, multi-expert clustering and multi-scale analysis. Within this context, the main difficulty of collaborative clustering is to determine how to weight the influence of the different clustering methods with the goal of maximizing the final results and minimizing the risk of negative collaborations—where the results are worse after collaboration than before. In this paper, we study how the quality and diversity of the different collaborators, but also the stability of the partitions can influence the final results. We propose both a theoretical analysis based on mathematical optimization, and a second study based on empirical results. Our findings show that on the one hand, in the absence of a clear criterion to optimize, a low diversity pool of solution with a high stability are the best option to ensure good performances. And on the other hand, if there is a known criterion to maximize, it is best to rely on a higher diversity pool of solution with a high quality on the said criterion. While our approach focuses on entropy based collaborative clustering, we believe that most of our results could be extended to other collaborative algorithms.

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“…The stopping criterion used by our algorithm is the probabilistic 613 confusion entropy [29,30] as shown in Eq. (16) bellow:…”

Section: Stopping Criterion 612mentioning

confidence: 99%

Entropy based probabilistic collaborative clustering

Sublime

Matei

Cabanès

et al. 2017

Pattern Recognition

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Unsupervised machine learning approa ches involving several clustering algorithms working together to tackle difficult data sets are a recent area of research with a large number of applications such as clustering of distributed data, multi-expert clustering, multi-scale clustering analysis or multi-view clustering. Most of these frameworks can be regrouped under the umbrella of collaborative clustering, the aim of which is to reveal the common underlying structures found by the different algorithms while analyzing the data. Within this context, the purpose of this article is to propose a collaborative framework lifting the limitations of many of the previously proposed methods: Our proposed collaborative learning method makes possible for a wide range of clustering algorithms from different families to work together based solely on their clustering solutions, thus lifting previous limitation requiring identical prototypes between the different collaborators. Our proposed framework uses a variational EM as its theoretical basis for the collaboration process and can be applied to any of the previously mentioned collaborative contexts. In this article, we give the main ideas and theoretical foundations of our method, and we demonstrate its effectiveness in a series of experiments on real data sets as well as data sets from the literature.

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Probabilistic Confusion Entropy for Evaluating Classifiers

Cited by 21 publications

References 44 publications

Probabilistic modeling and visualization for bankruptcy prediction

Probabilistic modeling and visualization for bankruptcy prediction

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Entropy based probabilistic collaborative clustering

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