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Is there a way to identify which dataset a value belongs to for overlapping datasets?

조회 수: 3 (최근 30일)
I have three types of datasets. These data sets visually shows that `data a` has comparatively lower values compared to `data b` and `data c`. I used a box plot to make a comparison and it shows that they have differences but there are overlaps. I will demonstrate them in the code below:
clc; clear all; close all
load("dataset.mat")
figure
hold on
xlabel('index of points')
ylabel('data value')
plot(a,'.',DisplayName='data1')
plot(b,'.',DisplayName='data2')
plot(c,'.',DisplayName='data3')
figure;
boxplot([a b c],'Notch','on','Labels',{'data1','data2','data3'})
grid on
Now considering these data sets, I have a set of values, say [4 7 40 8 4], I want to predict which dataset these value may belong to. Is there a way to do that? Having a very basic knowledge of statistics, I cannot come up with a solution. I found one solution based on which Kernel density estimate (kde) was used for comparison. However, the data was distinctly separable. In my case, the datasets are more overlapped, is there a way to predict in this case? Forgive my very basic knowledge and suggest a solution. Will appreciate it.
Thanks in advance.
figure
hold on
[fn,xfn,bwn] = kde(a);
plot(xfn,fn)
[fn,xfn,bwn] = kde(b);
plot(xfn,fn)
[fn,xfn,bwn] = kde(c);
plot(xfn,fn)
  댓글 수: 2
Jeff Miller
Jeff Miller 2024년 3월 25일
You might look into logistic regression and discriminant function analysis. These are both techniques for predicting category membership.
UH
UH 2024년 3월 25일
Thank you for the idea. I am looking into these.

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Chunru
Chunru 2024년 3월 25일
websave("dataset.mat", "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1650591/dataset.mat")
ans = '/users/mss.system.pbnsl/dataset.mat'
load("dataset.mat")
figure
hold on
xlabel('index of points')
ylabel('data value')
plot(a,'.',DisplayName='data1')
plot(b,'.',DisplayName='data2')
plot(c,'.',DisplayName='data3')
whos
Name Size Bytes Class Attributes a 90x1 720 double ans 1x35 70 char b 90x1 720 double c 90x1 720 double cmdout 1x33 66 char gdsCacheDir 1x14 28 char gdsCacheFlag 1x1 8 double i 0x0 0 double managers 1x0 0 cell managersMap 0x1 8 containers.Map
figure;
boxplot([a b c],'Notch','on','Labels',{'data1','data2','data3'})
grid on
x = [4 7 40 8 4]';
% K Nearest neighbour (KNN) classification
data = [a; b; c];
label = [ones(size(a)); 2*ones(size(b)); 3*ones(size(b)) ];
Mdl = fitcknn(data, label, "NumNeighbors", 80); % larger number of neighbours
predictedClass = predict(Mdl, x) % predicted class
predictedClass = 5x1
1 1 3 2 1
  댓글 수: 1
UH
UH 2024년 3월 25일
Thank you for your answer. I will further check with whether the most occuring prediction leads to the predicted class or I have to perform some additional analysis. This probably works. Thank you. Good day.

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