Probabilistic Neural Networks works better for classification problems

Published 02 May 08 04:30 PM | norman

If you have checked out my paper about the using of Feed-Forward Backpropagation Neural Networks, here's another approach that I used. It is called the Probabilistic Neural Networks. This approach can forecast the onset of Diabetes Mellitus with 100% accuracy. Compared to the Feed-Forward Backpropagation approach that could give no more than 93%.

Probabilistic Neural Networks was actually designed for such classification problems. No wonder.

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Filed under: Algorithm, Computational Intelligence, Neural Network

Comments

# arifin_tjia said on May 6, 2008 11:02 AM:

Did you know? that what kind of the algorithm in text mining and what they are?

I felt confuse in looking for algorithm in text mining thank before.

# adhiguna said on May 14, 2008 03:13 AM:

Great mas Norman, saya akan coba algoritma ini untuk aplikasi machine vision. Thanks untuk insightnya...

# acoltye said on May 22, 2008 08:55 PM:

back to matlab again boss ?!

sayangnya matlab 12 semakin sucks gara2 make Java,

berat dan boros memory (mempry limit up to 1.7 GB for 32 bit machine) ...

trus boss... hati2 dengan classifier yang kasih output akurasi 100%! itu kadang2 bukan indikasi yang baik, sebab bisa jadi classifier NN itu mengalami overfitting akibat overtraining pada dataset.

en.wikipedia.org/.../Overfitting

kalo di data mining bisa direduksi pake metode cross-validation (CV n-folds) en, ada baiknya algoritma ybs di uji lagi boss dengan dataset yang lebih besar.

@arifin

text mining juga lebar lho boss.. fungsi text mining nya apakah asosiasi (field information retrieval), sequence (field biogenetik) ? baru bisa ketahuan list algoritmanya.

@adhiguna

boss... NN yang dipakai Norman output-nya true or false...

kalo machine vision pasti outputnya akan lebih dari itu bukan ?

dan melihat kompleksitas dan cost komputasinya..  aku agak ragu jika NN workable untuk machine vision yang perlu response time yang singkat...

# adhiguna said on May 25, 2008 08:48 AM:

@Acoltye, machine vision kalau defect inspection outputnya cuman yes or no kok, cuman kalau masalah response time, memang NN biasanya computation costnya tinggi, so agak lambat, biasanya untuk real application, pakai K-Nearest Neighbor aja udah cukup kok.

Oh ya juga buat Norman, I think I am agree with Acoltye, akurasi 100 persen itu bisa jadi karena overfitting. Coba datanya pake yang lain deh.