Different thresholding methods on Nearest Shrunken Centroid algorithm
This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencySimilar Papers 제목 키워드 기반
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was deve…
Conformal PredictionData AugmentationPredictionIdentification of urinary biomarkers of food intake for onion by untargeted LC-MS metabolomics
Scope: Biomarkers of food intake (BFIs) are useful tools for objective assessment of food intake and compliance. The aim of this study was to discover and identify urinary BFIs for onion. Methods and results: In a random…
Classification with Nearest Disjoint Centroids
In this paper, we develop a new classification method based on nearest centroid, and it is called the nearest disjoint centroid classifier. Our method differs from the nearest centroid classifier in the following two asp…
Classificationfeature selectionSparse $\ell_1$ and $\ell_2$ Center Classifiers
The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest cent…
Classificationfeature selectionGeneral ClassificationSynthetic Reduced Nearest Neighbor Model for Regression
Nearest neighbor models are among the most established and accurate approaches to machine learning. In this paper, we investigate Synthetic Reduced Nearest Neighbor (SRNN) as a novel approach to regression tasks. Existin…
Binary Classificationmodelregression