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The DT performs the splitting process for classification until a leaf node is reached.
In [53] authors developed a data mining process for classification of imbalance data based on MapReduce to predict traffic accidents with the highway traffic data.
Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc).
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Those studies and the current study are similar in that they integrate various types of information, and all are similarity-based inference processes for classification or clustering.
Open image in new window Fig. 2 Two strategies of leveraging side views in feature selection process for graph classification: late fusion and early fusion [6].
Open image in new window Fig. 4 Two strategies of leveraging side views in feature selection process for graph classification: late fusion and early fusion.
This means that for gene expression databases only very few gene expressions are useful for the process of classification irrespective of the type of classifier employed.
Unlike rule based approaches of identification and classification, machine learning approaches have emerged much superior for the process of classification.
The objective is to reduce the number of process variables needed for classification by eliminating noisy and irrelevant ones.
Prototype selection is an important task for classifiers since through this selection process the time for classification or training could be reduced.
Matching sequences were classified by gene ontology using the TAIR GO annotation tool (http://www.arabidopsis.org/tools/bulk/go/index.jsp) and processed for GO classification and pathway enrichment using Blast2GO [ 61] using the entire GO-annotated collection of SR3 and JN177 ESTs as the reference.
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