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  • Hello My name is Thales Sehnrting and I will

  • present very breafly how the kNN algorithm works

  • kNN means k nearest neighbors It's a very simple algorithm, and given N

  • training vectors, suppose we have all these 'a' and 'o' letters as training vectors in

  • this bidimensional feature space, the kNN algorithm identifies the k nearest neighbors

  • of 'c' 'c' is another feature vector that we want

  • to estimate its class In this case it identifies the nearest neighbors

  • regardless of labels So, suppose this example we have k equal to

  • 3, and we have the classes 'a' and 'o' And the aim of the algorithm is to find the

  • class for 'c' If k is 3 we have to find the 3 nearest neighbors

  • of 'c' So, we can see that in this case the 3 nearest

  • neighbors of 'c' are these 3 elements here We have 1 nearest neighbor of class 'a', we

  • have 2 elements of the class 'o' which are near to 'c'

  • We have 2 votes for 'o' and 1 vote for 'a' In this case, the class of the element 'c'

  • is going to be 'o' This is very simple how the algorithm k nearest

  • neighbors works Now, this is a special case of the kNN algorithm,

  • is that when k is equal to 1 So, we must try to find the nearest neighbor

  • of the element that will define the class And to represent this feature space, each

  • training vector will define a region in this feature space here

  • And a property that we have is that each region is defined by this equation

  • We have a distance between each element x and x_i, that have to be smaller than the

  • same distance for each other element In this case it will define a Voronoi partition

  • of the space, and can be defined, for example, this element 'c' and these elements 'b', 'e'

  • and 'a' will define these regions, very specific regions

  • This is a property of the kNN algorithm when k is equal to 1

  • We define regions 1, 2, 3 and 4, based on the nearest neighbor rule

  • Each element that is inside this area will be classified as 'a', as well as each element

  • inside this area will be classified as 'c' And the same for the region 2 and region 3,

  • for classes 'e' and 'b' as well Now I have just some remarks about the kNN

  • We have to chose and odd value of k if you have a 2-class problem

  • This happens because when we have a 2-class and if we set k equal to 2, for example, we

  • can have a tie What will be the class? The majority class

  • inside the nearest neighbors? So, we have always to set odd values for a

  • 2-class problem And also the value of k must not be a multiple

  • of the number of classes, it is also to avoid ties

  • And we have to remember that the main drawback of this algorithm is the complexity in searching

  • the nearest neighbors for each sample The complexity is a problem because we have

  • lots of elements, in the case of a big dataset we will have lots of elements

  • And we will have to search the distance between each element to the element that we want to

  • classify So, for a large dataset, this can be a problem

  • Anyhow, this kNN algorithm produces good results So, this is the reference I have used to prepare

  • this presentation Thanks for your attention, and this is very

  • breafly how the kNN algorithm works

Hello My name is Thales Sehnrting and I will

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B1 US nearest algorithm element class define feature

How kNN algorithm works

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    Jarne posted on 2019/11/18
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