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One such method is classification of compounds based on their molecular scaffolds, a concept widely used by medicinal chemists to group molecules of similar properties.
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However, the method is for classification on large image blocks and does not deal with small tissue cores or IHC images.
Another classification method is the Pruzansky classification that has been used more frequently because it has more detailed grading of mandibular shape [10] (Table 1).
The method is for each classification of continuous cumulative values, and it has a good classification effect.
Furthermore, a hierarchical clustering method followed by a random forest classification method is proposed to boost the classification performance among confusing classes.
Experiments state that our method is effective for classification.
This method is a supervised classification method implementing a fuzzy partition algorithm [14].
Our method is compared with classification models commonly used in medical applications.
To determine whether the protein is properly removed from the cluster by the filtering method is a binary classification problem.
The method is based on a classification algorithm that uses an ensemble of different classification trees, called Random Forest.
A classification method is used to measure the distance.
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