On Selecting a Probabilistic Classifier for Appointment No-show Prediction

2020 ◽  
Author(s):  
Shannon Harris ◽  
Michele Samorani
Keyword(s):  
Probabilistic Classifier

scholarly journals A heuristic in A* for inference in nonlinear Probabilistic Classifier Chains

2017 ◽  
Vol 126 ◽  
pp. 78-90 ◽  
Author(s):  
Deiner Mena ◽  
José Ramón Quevedo ◽  
Elena Montañés ◽  
Juan José del Coz
Keyword(s):  
Probabilistic Classifier ◽  
Classifier Chains

A Probabilistic Classifier System and Its Application in Data Mining

2006 ◽  
Vol 14 (2) ◽  
pp. 183-221 ◽  
Author(s):  
Jorge Muruzábal
Keyword(s):  
Data Mining ◽  
Predictive Power ◽  
Benchmark Problems ◽  
Classifier System ◽  
Probabilistic Classifier ◽  
Classification Tasks ◽  
Representation Language ◽  
Stable Learning ◽  
Well Posed ◽  
Low Uncertainty

The article is about a new Classifier System framework for classification tasks called BYP CS (for BaYesian Predictive Classifier System). The proposed CS approach abandons the focus on high accuracy and addresses a well-posed Data Mining goal, namely, that of uncovering the low-uncertainty patterns of dependence that manifest often in the data. To attain this goal, BYP CS uses a fair amount of probabilistic machinery, which brings its representation language closer to other related methods of interest in statistics and machine learning. On the practical side, the new algorithm is seen to yield stable learning of compact populations, and these still maintain a respectable amount of predictive power. Furthermore, the emerging rules self-organize in interesting ways, sometimes providing unexpected solutions to certain benchmark problems.

scholarly journals A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates

2019 ◽  
Vol 33 (6) ◽  
pp. 1674-1709 ◽  
Author(s):  
James Large ◽  
Jason Lines ◽  
Anthony Bagnall
Keyword(s):  
Weighting Scheme ◽  
Classifier Ensemble ◽  
Probabilistic Classifier

AN EFFECTIVE PORNOGRAPHIC WEB FILTERING SYSTEM USING A PROBABILISTIC CLASSIFIER

2005 ◽  
Author(s):  
Polpinij JANTIMA ◽  
Puangpronpitag SUMNUK ◽  
Sibunruang CHUMSAK ◽  
Chamchong RAPEEPORN ◽  
Chotthanom ANIRUT
Keyword(s):  
Probabilistic Classifier

scholarly journals Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Zhixin Yang ◽  
Pak Kin Wong ◽  
Chi Man Vong ◽  
Jianhua Zhong ◽  
JieJunYi Liang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Diagnostic System ◽  
Kernel Principal Component Analysis ◽  
Training Dataset ◽  
Decision Threshold ◽  
Turbine Generator ◽  
Single Fault ◽  
Probabilistic Classifier

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

scholarly journals Two-stage Training for Learning from Label Proportions

2021 ◽  
Author(s):  
Jiabin Liu ◽  
Bo Wang ◽  
Xin Shen ◽  
Zhiquan Qi ◽  
Yingjie Tian
Keyword(s):  
Performance Improvement ◽  
Training Data ◽  
Continuous Training ◽  
High Entropy ◽  
Training Stage ◽  
Probabilistic Classifier ◽  
Leibler Divergence ◽  
Learning From Label Proportions ◽  
Post Hoc ◽  
The Given

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and posterior class distributions. However, the unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions. Besides, concerning the probabilistic classifier, this strategy unavoidably results in high-entropy conditional class distributions at the instance level. These issues further degrade the performance of the instance-level classification. In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. In addition, we introduce the mixup strategy and symmetric cross-entropy to further reduce the label noise. Our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when incorporated into other deep LLP models as a post-hoc phase.

Improving the $$\epsilon $$-approximate algorithm for Probabilistic Classifier Chains

2020 ◽  
Vol 62 (7) ◽  
pp. 2709-2738
Author(s):  
Miriam Fdez-Díaz ◽  
Laura Fdez-Díaz ◽  
Deiner Mena ◽  
Elena Montañés ◽  
José Ramón Quevedo ◽  
...  
Keyword(s):  
Approximate Algorithm ◽  
Probabilistic Classifier ◽  
Classifier Chains
Sign in / Sign up

Export Citation Format

Share Document