Pairwise Optimization of Bayesian Classifiers for Multi-class Cost-Sensitive Learning

Charnay, Clément; Lachiche, Nicolas; Braud, Agnès

doi:10.1109/ictai.2013.80

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…The results of the network cost model that was performed in this investigation allow for the conclusion that the simulation allowed for the effective evaluation of the cost of the network faults, as well as the ability to reduce the total misclassification cost. Previous research showed that it was possible to measure the distribution and the network faults as a means of identifying the cause of those faults [2]. In addition, the results of this investigation also allow for the conclusion that it is possible to minimise expected costs with the use of J48 rules as a means of minimising the expected operation costs.…”

Section: Resultsmentioning

confidence: 79%

“…At the same time, analogue technology was used for the stand-alone local network, while digital facilities were used for toll and enhanced services. Once again, the idea was to separate and define the costs based on the types of services provided to either home customers or business customers [2].…”

Section: Cost Algorithm Methodology: Measuring Performance Of Individmentioning

confidence: 99%

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Financial Predictions Using Cost Sensitive Neural Networks for Multi-Class Learning

Rozaki

2016

AEF

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The interest in the localisation of wireless sensor networks has grown in recent years. A variety of machine-learning methods have been proposed in recent years to improve the optimisation of the complex behaviour of wireless networks. Network administrators have found that traditional classification algorithms may be limited with imbalanced datasets. In fact, the problem of imbalanced data learning has received particular interest. The purpose of this study was to examine design modifications to neural networks in order to address the problem of cost optimisation decisions and financial predictions. The goal was to compare four learning-based techniques using cost-sensitive neural network ensemble for multiclass imbalance data learning. The problem is formulated as a combinatorial cost optimisation in terms of minimising the cost using meta-learning classification rules for Naïve Bayes, J48, Multilayer Perceptions, and Radial Basis Function models. With these models, optimisation faults and cost evaluations for network training are considered.

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Section: Resultsmentioning

confidence: 79%

Section: Cost Algorithm Methodology: Measuring Performance Of Individmentioning

confidence: 99%

Financial Predictions Using Cost Sensitive Neural Networks for Multi-Class Learning

Rozaki

2016

AEF

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“…According to the deployment environment, typically a matrix of misclassification costs and the prior probability of the classes, this threshold can be tuned and the model is adapted for class prediction. Research has also been done to handle this problem for multi-class classification [1], [2].…”

Section: Related Workmentioning

confidence: 99%

“…Several methods [1], [2], [10], [11] have been proposed to use the learnt model in different deployment environments by adjusting the output values. However, an alternative is to transform the input values before using the existing model.…”

Section: Introductionmentioning

confidence: 99%

Reframing Continuous Input Attributes

Ahmed

Lachiche

Charnay

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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Reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly because the operating context can be expected to vary from training to deployment. Dataset shift is a crucial example of this where training and testing datasets follow different distributions. However, most of the existing dataset shift solving algorithms need costly retraining operation and are not suitable to use the existing model. In this paper, we propose a new approach called reframing to handle dataset shift. The main objective of reframing is to build a model once and make it workable without retraining. We propose two efficient reframing algorithms to learn the optimal shift parameter values using only a small amount of labelled data available in the deployment. Thus, they can transform the shifted input attributes with the optimal parameter values and use the same existing model in several deployment environments without retraining. We have addressed supervised learning tasks both for classification and regression. Extensive experimental results demonstrate the efficiency and effectiveness of our approach compared to the existing solutions. In particular, we report the existence of dataset shift in two real-life datasets. These real-life unknown shifts can also be accurately modeled by our algorithms.

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“…In pattern recognition problems, we try to design a classification function to predict the class label of a data sample, so that the misclassification errors of a set of training samples can be minimized [3,11,12,20,30,4]. A popular assumption for the learning of a classifier is that the loss of misclassifying any data sample in the training set is equal.…”

Section: Introductionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time.

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Pairwise Optimization of Bayesian Classifiers for Multi-class Cost-Sensitive Learning

Cited by 5 publications

References 12 publications

Financial Predictions Using Cost Sensitive Neural Networks for Multi-Class Learning

Financial Predictions Using Cost Sensitive Neural Networks for Multi-Class Learning

Reframing Continuous Input Attributes

Online classifier adaptation for cost-sensitive learning

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