Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine

Wang, Huazhen; Lin, Chen; Yang, Fan; Hu, Xueqin

doi:10.1016/j.compbiomed.2009.02.002

Cited by 17 publications

(8 citation statements)

References 22 publications

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“…De Lobel et al [54] have used RF as a pre-screening method to remove noisy SNPs before multifactor-dimensionality reduction in genetic association studies. Additionally, RF has been incorporated in a transductive confidence machine [95], a framework that allows the prediction of classifiers to be complemented with a confidence value that can be set by the user prior to classification [103]. …”

Section: Rf In the Life Sciencesmentioning

confidence: 99%

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

Touw¹,

Bayjanov²,

Overmars³

et al. 2012

Briefings in Bioinformatics

337

281

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In the Life Sciences ‘omics’ data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.

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Section: Rf In the Life Sciencesmentioning

confidence: 99%

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

Touw¹,

Bayjanov²,

Overmars³

et al. 2012

Briefings in Bioinformatics

337

281

View full text Add to dashboard Cite

show abstract

“…Thus the corresponding nonconformity score will be rather small, and vice versa. Therefore, the nonconformity measurement can exactly reflect the nonconformity of the example [36], [37].…”

Section: Methodsmentioning

confidence: 99%

“…The method which combines CP and RF, namely CP-RF, was firstly proposed to deal with single-label classification problem in our previous work [36]. Unfortunately, CP-RF cannot be directly applied to syndrome differentiation of CF for it is a MLL problem.…”

Section: Introductionmentioning

confidence: 99%

Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine

Wang

Liu

et al. 2014

PLoS ONE

Self Cite

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ObjectiveChronic Fatigue (CF) still remains unclear about its etiology, pathophysiology, nomenclature and diagnostic criteria in the medical community. Traditional Chinese medicine (TCM) adopts a unique diagnostic method, namely ‘bian zheng lun zhi’ or syndrome differentiation, to diagnose the CF with a set of syndrome factors, which can be regarded as the Multi-Label Learning (MLL) problem in the machine learning literature. To obtain an effective and reliable diagnostic tool, we use Conformal Predictor (CP), Random Forest (RF) and Problem Transformation method (PT) for the syndrome differentiation of CF.Methods and MaterialsIn this work, using PT method, CP-RF is extended to handle MLL problem. CP-RF applies RF to measure the confidence level (p-value) of each label being the true label, and then selects multiple labels whose p-values are larger than the pre-defined significance level as the region prediction. In this paper, we compare the proposed CP-RF with typical CP-NBC(Naïve Bayes Classifier), CP-KNN(K-Nearest Neighbors) and ML-KNN on CF dataset, which consists of 736 cases. Specifically, 95 symptoms are used to identify CF, and four syndrome factors are employed in the syndrome differentiation, including ‘spleen deficiency’, ‘heart deficiency’, ‘liver stagnation’ and ‘qi deficiency’.The ResultsCP-RF demonstrates an outstanding performance beyond CP-NBC, CP-KNN and ML-KNN under the general metrics of subset accuracy, hamming loss, one-error, coverage, ranking loss and average precision. Furthermore, the performance of CP-RF remains steady at the large scale of confidence levels from 80% to 100%, which indicates its robustness to the threshold determination. In addition, the confidence evaluation provided by CP is valid and well-calibrated.ConclusionCP-RF not only offers outstanding performance but also provides valid confidence evaluation for the CF syndrome differentiation. It would be well applicable to TCM practitioners and facilitate the utilities of objective, effective and reliable computer-based diagnosis tool.

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“…Considering outlyingness of an example, in this way, relative to all cases within a class, however, does not make adequate use of local neighborhood patterns (where, arguably, tree based models derive much of their advantage from). The authors, in another paper [25], propose a nonconformity measure defined similarly to k-nearest neighbor conformal predictors [17,22], but with distance determined by inverse RF proximity. Reference [8] suggests a simple and intuitive measure of nonconformity based on the proportion of trees that correctly classify an example, and compares this with the k-proximity measure of [25].…”

Section: Random Forestsmentioning

confidence: 99%

“…The development of conformal prediction for k-nearest neighbors and support vector machines as underlying learning algorithms has been described in [22]. It has been applied with neural networks [16], regression, and recent work considers its use with random forests [8,25,26]. Most of this work uses CP in a transductive inference setting, which requires model re-learning for each new example to be predicted, and can be computationally burdensome.…”

Section: Introductionmentioning

confidence: 99%

Confidence in predictions from random tree ensembles

Bhattacharyya

2013

Knowl Inf Syst

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Obtaining an indication of confidence of predictions is desirable for many data mining applications. Predictions complemented with confidence levels can inform on the certainty or extent of reliability that may be associated with the prediction. This can be useful in varied application contexts where model outputs form the basis for potentially costly decisions, and in general across risk sensitive applications. The conformal prediction framework presents a novel approach for obtaining valid confidence measures associated with predictions from machine learning algorithms. Confidence levels are obtained from the underlying algorithm, using a nonconformity measure which indicates how 'atypical' a given example set is. The non-conformity measure is key to determining the usefulness and efficiency of the approach. This paper considers inductive conformal prediction in the context of random tree ensembles like random forests, which have been noted to perform favorably across problems. Focusing on classification tasks, and considering realistic data contexts including class imbalance, we develop non-conformity measures for assessing the confidence of predicted class labels from random forests. We examine the performance of these measures on multiple datasets. Results demonstrate the usefulness and validity of the measures, their relative differences, and highlight the effectiveness of conformal prediction random forests for obtaining predictions with associated confidence.

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Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine

Cited by 17 publications

References 22 publications

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine

Confidence in predictions from random tree ensembles

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