scholarly journals Assessment of prediction ability for reduced probabilistic neural network in data classification problems

2016 ◽  
Vol 21 (1) ◽  
pp. 199-212 ◽  
Author(s):  
Maciej Kusy ◽  
Jacek Kluska
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Data Classification ◽  
Classification Problems ◽  
Prediction Ability

scholarly journals Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems

2015 ◽  
Vol 61 (3) ◽  
pp. 289-300 ◽  
Author(s):  
Maciej Kusy
Keyword(s):  
Neural Network ◽  
Dimensionality Reduction ◽  
Probabilistic Neural Network ◽  
Extraction Procedure ◽  
Principal Component ◽  
Training Data ◽  
Data Sets ◽  
Classification Problems ◽  
Prediction Ability ◽  
Feature Selection Approach

Abstract This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN models are explored. The prediction ability of PNNs trained on original and reduced data sets is determined with the use of a 10-fold cross validation procedure.

Improved water cycle algorithm with probabilistic neural network to solve classification problems

2020 ◽  
Vol 23 (4) ◽  
pp. 2703-2718 ◽  
Author(s):  
Mohammed Alweshah ◽  
Maria Al-Sendah ◽  
Osama M. Dorgham ◽  
Ammar Al-Momani ◽  
Sara Tedmori
Keyword(s):  
Neural Network ◽  
Water Cycle ◽  
Probabilistic Neural Network ◽  
Classification Problems ◽  
Water Cycle Algorithm

Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3760-3765 ◽  
Author(s):  
Ze Yue Wu ◽  
Yue Hui Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Subcellular Localization ◽  
Classification Problem ◽  
Research Field ◽  
Protein Subcellular Localization ◽  
Classification Problems ◽  
Prediction Ability ◽  
Increment Of Diversity ◽  
Artificial Neural

Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible and effective.

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