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These measures can highlight the manner in which classes are separated or interleaved, which is a critical factor for classification accuracy.
Since the impact of off-farm income on technology adoption is well-reported, the present study also used off-farm income as a factor for classification (Savadogo et al. [1998]; Nehring et al. [2005]; Fernandez-Cornejo et al. [2005]).
The relative feature importance for discrimination showed that Rv3407 was by far the most influential factor for classification (figure 3C).
Random forest analyses together with leave-1-out cross-validations across all possible combinations were applied to determine the prediction accuracy and relative importance of each factor for classification.
These observations of ours are consistent with the sixth TNM classification for HCC revised by UICC that the number of nodes, but not size of HCC, is the crucial factor for classification and prognosis of HCC (Poon and Fan, 2003).
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Most of the farm typology study has focused on socio-economic and agro-ecological factors for classification of farms.
Indian Council of Agricultural Research ICARR) used several factors for classification of farming systems, but the bases were more discussion-based with limited statistical applications, perhaps due to functional purpose of the classification (Sanjeev et al. [2010]).
The AECG classification criteria [ 12] have revised rules to classify primary SS: namely, positive histopathology or serology is necessary when all six items are factors for classification.
According to the PC2 axis, the highest and the lowest cortisol levels and the difference between them were the most important factors for classification of the patients.
From April 2002 to May 2008, 1139 patients aged 1 18 years with newly diagnosed BCP-ALL (standard risk, SR = 457, high risk, HR = 543, and extremely high risk, ER = 139) (risk factors for classification are described in Table S 1) were enrolled in the JACLS ALL study and assigned to three risk-stratified ALL02 protocols [ 26, 27].
Towards handling larger HTS assays and exploiting the set of common active interactions as a factor for improving classification performance, we explore formulating the problem as a multi-label classification (MLC) instead of the conventional binary classification setup.
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