r-Nearest neighbors are a modified version of the k-nearest neighbors. The issue with k-nearest neighbors is the choice of k. With a smaller k, the classifier would be more sensitive to outliers. If the value of k is large, then the classifier would be including many points from other classes. It is from this logic that we get the r near neighbors algorithm.
Intuition:
Consider the following data, as the training set.
The green color points belong to class 0 and the red color points belong to class 1. Consider the white point P as the query point whose
If we take the radius of the circle as 2.2 units and if a circle is drawn using the point P as the center of the circle, the plot would be as follows
As the number of points in the circle belonging to class 1 (5 points) is greater than the number of points belonging to class 0 (2 points)
Algorithm:
Step 1: Given the point P, determine the sub-set of data that lies in the ball of radius r centered at P,
Br (P) = { Xi ∊ X | dist( P, Xi ) ≤ r }Step 2: If Br (P) is empty, then output the majority class of the entire data set.
Step 3: If Br (P) is not empty, output the majority class of the data points in it.
Implementation of the r radius neighbors algorithm is as follows::
C++
// C++ program to implement the // r nearest neighbours algorithm. #include <bits/stdc++.h> using namespace std; struct Point { // Class of point int val; // Co-ordinate of point double x, y; }; // This function classifies the point p using // r k nearest neighbour algorithm. It assumes only // two groups and returns 0 if p belongs to class 0, else // 1 (belongs to class 1). int rNN(Point arr[], int n, float r, Point p) { // frequency of group 0 int freq1 = 0; // frequency of group 1 int freq2 = 0; // Check if the distance is less than r for (int i = 0; i < n; i++) { if ((sqrt((arr[i].x - p.x) * (arr[i].x - p.x) + (arr[i].y - p.y) * (arr[i].y - p.y))) <= r) { if (arr[i].val == 0) freq1++; else if (arr[i].val == 1) freq2++; } } return (freq1 > freq2 ? 0 : 1); } // Driver code int main() { // Number of data points int n = 10; Point arr[n]; arr[0].x = 1.5; arr[0].y = 4; arr[0].val = 0; arr[1].x = 1.8; arr[1].y = 3.8; arr[1].val = 0; arr[2].x = 1.65; arr[2].y = 5; arr[2].val = 0; arr[3].x = 2.5; arr[3].y = 3.8; arr[3].val = 0; arr[4].x = 3.8; arr[4].y = 3.8; arr[4].val = 0; arr[5].x = 5.5; arr[5].y = 3.5; arr[5].val = 1; arr[6].x = 5.6; arr[6].y = 4.5; arr[6].val = 1; arr[7].x = 6; arr[7].y = 5.4; arr[7].val = 1; arr[8].x = 6.2; arr[8].y = 4.8; arr[8].val = 1; arr[9].x = 6.4; arr[9].y = 4.4; arr[9].val = 1; // Query point Point p; p.x = 4.5; p.y = 4; // Parameter to decide the class of the query point float r = 2.2; printf("The value classified to query point" " is: %d.\n", rNN(arr, n, r, p)); return 0; } |
Python3
# Python3 program to implement the # r nearest neighbours algorithm. import math def rNN(points, p, r = 2.2): ''' This function classifies the point p using r k nearest neighbour algorithm. It assumes only two groups and returns 0 if p belongs to class 0, else 1 (belongs to class 1). Parameters - points : Dictionary of training points having two keys - 0 and 1. Each class have a list of training data points belonging to them p : A tuple, test data point of form (x, y) k : radius of the r nearest neighbors ''' freq1 = 0 freq2 = 0 for group in points: for feature in points[group]: if math.sqrt((feature[0]-p[0])**2 + (feature[1]-p[1])**2) <= r: if group == 0: freq1 += 1 elif group == 1: freq2 += 1 return 0 if freq1>freq2 else 1# Driver function def main(): # Dictionary of training points having two keys - 0 and 1 # key 0 have points belong to class 0 # key 1 have points belong to class 1 points = {0:[(1.5, 4), (1.8, 3.8), (1.65, 5), (2.5, 3.8), (3.8, 3.8)], 1:[(5.5, 3.5), (5.6, 4.5), (6, 5.4), (6.2, 4.8), (6.4, 4.4)]} # query point p(x, y) p = (4.5, 4) # Parameter to decide the class of the query point r = 2.2 print("The value classified to query point is: {}".format( rNN(points, p, r))) if __name__ == '__main__': main() |
The value classified to query point is: 1.
Other techniques like kd-tree and locality-sensitive hashing can be used to reduce the time complexity of finding the neighbors.
Applications: This algorithm can be used to identify outliers. If a pattern does not have any similarity with the patterns within the radius chosen, it can be identified as an outlier.
