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I am working on a data mining projects and I have to design following model.

I have given 4 feature x1, x2, x3 and x4 and four function defined on these

feature such that each function depend upon some subset of available feature.

e.g.

F1(x1, x2) =x1^2+2x2^2

F2(x2, x3) =2x2^2+3x3^3

F3(x3, x4) =3x3^3+4x4^4

This implies F1 is some function which depend on feature x1, x2. F2 is some feature which depend upon x2, x3 and so on

Now I have a training data set is available where value of x1,x2,x3,x4 is known and sum(F1+F2+F3) { I know total sum but not individual sum of function)

Now using these training data I have to make a model which which can predict total sum of all the function correctly i.e. (F1+F2+F3)

I am new to the data mining and Machine learning field .So I apologizes in advance if this question is too trivial or wrong. I have tried to model it many way but I am not getting any clear thought about it. I will appreciate any help regarding this.

Ish Yadav
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1 Answers1

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Your problem is non-linear regression. You have features

x1 x2 x3 x4 S where (S = sum(F1+F2+F3) )

You would like to predict S using Xn but S function is non-linear. Since your function S is non linear, you need to use a non-linear regression algorithm for this problem. Normal nonlinear regression may solve your problem or you may choose other approaches. You may for example try Tree Regression or MARS (Multivariate Adaptive Regression Splines). They are well known algorithms and you can find commercial and open source versions.

Atilla Ozgur
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