Multi-TGDR: A Regularization Method for Multi-Class Classification in Microarray Experiments
نویسندگان
چکیده
منابع مشابه
Multi-TGDR: A Regularization Method for Multi-Class Classification in Microarray Experiments
BACKGROUND As microarray technology has become mature and popular, the selection and use of a small number of relevant genes for accurate classification of samples has arisen as a hot topic in the circles of biostatistics and bioinformatics. However, most of the developed algorithms lack the ability to handle multiple classes, arguably a common application. Here, we propose an extension to an e...
متن کاملExploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملA heuristic method for consumable resource allocation in multi-class dynamic PERT networks
This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a n...
متن کامل2 a Nonparametric Multi Class Partitioning Method for Classification
c classes are characterized by unknown probability distributions. A data sample containing labelled vectors from each of the c classes is available. The data sample is divided into test and training samples. A classifier is designed based on the training sample and evaluated with the test sample. The classifier is also evaluated based on its asymptotic properties as sample size increases. A mul...
متن کاملImmune Centroids Over-Sampling Method for Multi-Class Classification
To improve the classification performance of imbalanced learning, a novel over-sampling method, Global Immune Centroids OverSampling (Global-IC) based on an immune network, is proposed. GlobalIC generates a set of representative immune centroids to broaden the decision regions of small class spaces. The representative immune centroids are regarded as synthetic examples in order to resolve the i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS ONE
سال: 2013
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0078302