# What does $w_{ni}$ mean in the weighted nearest neighbour classifier?

Wiki gives this definition of KNN

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:

• In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object
being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
• In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors.

and this explanation about "The weighted nearest neighbour classifier"

The k-nearest neighbour classifier can be viewed as assigning the k nearest neighbours a weight 1/k and all others 0 weight. This can be generalised to weighted nearest neighbour classifiers. That is, where the ith nearest neighbour is assigned a weight $${\displaystyle > w_{ni}}$$, with $${\displaystyle \sum _{i=1}^{n}w_{ni}=1}$$. An analogous result on the strong consistency of weighted nearest neighbour classifiers also holds.

this $$w_{ni}$$ confuses me a lot.

taking the iris dataset as the example, there are 150 data points, if we choose k=50, are there top 50 neighbours, each of which has a 1/50 weight, and the other 100 data points 0 weight.

if it is, $$w_i = 1/50$$ where $$i=1, 2, ..., 50$$.

so, what does $$w_{ni}$$ mean?

Assume $$𝑤_{𝑛1}$$ represents the nearest neighbor, $$𝑤_{𝑛2}$$ represents the second nearest neighbor, what does "n" in $$𝑤_{𝑛i}$$=1/50 mean? Does $$𝑤_{11}$$mean the first example weights 1/50 to first class, say, Iris Setosa?

In weighted nearest neighbour, all of the points in the dataset have a contribution, which is quantified by $$w_{ni}$$ in your references. So, in this version, there is no top $$k$$. Every point in the dataset has a saying on the outcome. And, KNN can also be considered as a special case of weighted NN where $$w_{ni}=1/k$$ when $$1\leq i\leq k$$, and $$0$$ otherwise. In regression, these weights can be directly incorporated to the outcome, i.e. $$\hat{y}=\sum_{i=1}^nw_{ni}y_i$$ But in classification, you'll count votes together with weights, i.e. the cumulative vote for class $$m$$ is calculated as follows: $$c_m=\sum_{i=1}^nw_{ni}\underbrace{\mathbf{1}[y_i=m]}_{\text{indicator function}}=\sum_{y_i=m}w_{ni}$$ In the end, you'll compare the votes on classes. And, decide the class with the highest vote.
Taking your example, i.e. the iris dataset, and a KNN with $$k=50$$, we have $$w_{ni}=1/50$$ for $$1\leq i \leq 50$$, as you noted. So, the top $$50$$ nearest neighbors have equal vote on the outcome. Iris dataset has three classes, so you'll calculate $$c_1,c_2,c_3$$ and choose the one with the highest value.
• Thanks for your answer! Assume $w_{n1}$ represents the nearest neighbor, $w_{n2}$ represents the second nearest neighbor, what does "n" in $w_{ni}=1/50$ mean? What does $w_{11}$ mean the first example weights 1/50 to first class, say, Iris Setosa? – fu DL Aug 18 '19 at 3:22