As shown in Table 6, the hybrid model has increased the sensitivity ~3% to 4% but the prediction accuracy was nearly same as that of MACCS fingerprints based classification model.
Based on that, we have developed a webserver using SVM based classification model.
The Bayesian based classification model developed by Ekins et. al. has good predictive power value >0.7 (in-term of AUC) on independent dataset [9].
Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size.
In our previous work a sparse representation based classification model was proposed.
This study investigates the use of a support vector machine based classification model for determining the prognosis of ICU patients with haematological malignancies by comparing it with a logistic regression based classification model.
The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset.
It was decided to develop online cardiac chest pain prognostic models based on LR based classification setups which are shown in Table 15.
As the conditions for a software module to have defects are hard to identify, machine learning based classification models are still developed to approach the problem of defect prediction.
We extracted the type of model based on the classification mentioned above.
The building and testing of comparison model based on the classification model framework can be accomplished using the following steps: 1) Sample positive and negative target genes data as a training model of the data set, from positive target genes (788 groups) and negative target genes (4000 groups) randomly, with total of 4788 target genes.
