Multiprobabilistic prediction in early medical diagnoses

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Zhan

Guan

et al. 2018

“…In addition, PAC learning does not provide any information on the reliability of individual prediction [10]. In contrast, Bayesian learning and other probability algorithms, such as logistic regression [11] and Platt’s method [12], can complement every individual prediction with information of probability to indicate how every potential label is correct. However, the main disadvantage of these algorithms is that they depend on a strong statistical assumption for the model.…”

Section: Introductionmentioning

confidence: 99%

“…Conformal prediction was proposed and developed by Vladimir Vovk and his co-workers since 2005 [10,11,19,20]. It is based on a consistent and well-defined mathematical framework and measures how well the new example is conformed to the group of observations.…”

Section: Introductionmentioning

confidence: 99%

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

Zhan

Sun

Miao

et al. 2017

Self Cite

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.

“…In this work, a flexible machine learning framework, Venn machine [ 12 , 13 , 14 ] was introduced to make valid probabilistic prediction for different species of ginseng samples, which means the estimated probability is unbiased. The only assumption required for Venn machine is that the example distribution is an independent identical distribution (I.I.D assumption), which can be easily satisfied by E-nose data, and do not need specific distribution of E-nose data Once the I.I.D assumption is satisfied, the validity of predicted probabilities is guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

Wang

Miao

Lyu

et al. 2016

Self Cite

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.