$2$ Coding

You can create a new classifier by implementing a sliding window kNN from

scratch, or you can create a copy ${?}^2$ and adapt one of the existing $kNN$ im$-$

plementations. However, we strongly recommend that you use the existing

implementations. Notice that your kNN adaptation must be implemented in a

way that it can be executed as a stand$-$alone classifier.

Standard kNN$.$ A standard implementation of $kNN$ for data streams main$-$

tains a single sliding window $W$ with the latest $N$ instances seen. The pre$-$

diction hat$(y)$ for an unlabelled instance $x$ is the most common label among the $k$

closest instances $($nearest neighbors$)$ to $x$ in $W,$ i$.$e$.$ neighbour $(x,k,W).$ This

process is shown in Figure $2,$ where all instances $($red and blue circles$)$ belong

to $W.$

$2\mathrm{.}1$ Rocchio Classifier with sliding window

Rocchio classifier $($aka Nearest Centroid Classifier$)$ is a classification model

based on the centroids $($mean$)$ of the training samples. In contrast to stan$-$

dard $kNN,$ you are asked to use centroids instead of individual instances to

calculate the distance and find the nearest neighbors. Each class label will be

represented by a centroid. Notice that there is no need to re$-$calculate the cen$-$

troids from the scratch for each arriving instance, as you can use the techniques

from the second week's class to incrementally update centroids instance by in$-$

stance.

Predictions. A prediction will be the label of the closest centroid. Consider

a standard $kNN$ classifier with sliding window $W,$ where we use $k=1.$ In

${?}^2$ You will need the original $kNN$ implementation for the experiments, so do not overide it