Title of article :
Linear separability and classification complexity
Author/Authors :
Elizondo، نويسنده , , David A. and Birkenhead، نويسنده , , Ralph and Gamez، نويسنده , , Matias and Garcia، نويسنده , , Noelia and Alfaro، نويسنده , , Esteban، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
12
From page :
7796
To page :
7807
Abstract :
We study the relationship between linear separability and the level of complexity of classification data sets. Linearly separable classification problems are generally easier to solve than non linearly separable ones. This suggests a strong correlation between linear separability and classification complexity. We propose a novel and simple method for quantifying the complexity of the classification problem. The method, which is shown below, reduces any two class classification problem to a sequence of linearly separable steps. The number of such reduction steps could be viewed as measuring the degree of non-separability and hence the complexity of the problem. This quantification in turn can be used as a measure for the complexity of classification data sets. Results obtained using several benchmarks are provided.
Keywords :
Linear separability , Classification , Non linear separability , Complexity
Journal title :
Expert Systems with Applications
Serial Year :
2012
Journal title :
Expert Systems with Applications
Record number :
2352011
Link To Document :
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