Protein classification and the k-Nearest Neighbors algorithm
The k-Nearest Neighbors (kNN) algorithm is one of the lazy learners that postpones the learning until the test tuple or test instance is provided.
A single training tuple is represented by a point in an n-dimensional space. In other words, n attributes' combinations are used to represent the specific training tuple. There is no specific training before the arrival of the test tuple that needs to be classified. Some preprocessing steps are needed, such as normalization for some attributes with large values compared to other attributes' values. Data normalization approaches in the data transformation can be applied here for preprocessing.
When a test tuple is given, the k-nearest training ...
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