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From: loren@tristan.llnl.gov (Loren Petrich)
Newsgroups: comp.ai.neural-nets
Subject: Distinguishing "Normal" from "Abnormal" Data
Message-ID: <64712@lll-winken.LLNL.GOV>
Date: 13 Jul 90 22:37:11 GMT
Sender: usenet@lll-winken.LLNL.GOV
Reply-To: loren@tristan.llnl.gov (Loren Petrich)
Organization: Lawrence Livermore National Laboratory
Lines: 33


	I may have asked about this earlier, and I am asking about
this again. I hope to use Neural Nets to analyze astronomical data,
and for this purpose, it will be vitally important to distinguish
"normal" and "abnormal" phenomena. I mean by "normal" anything that is
very commonplace; "abnormal" anything that is relatively rare. Since
the "abnormal" phenomena are sometimes the most interesting ones, it
will be vital to pick them out. I even think it may be better to risk
misclassifying some "normal" phenomena as "abnormal" than the other
way around.

	Has anyone else faced similar problems?

	What is the most efficient way to solve such problems?

	Is a backprop network a good thing to use, and if so, what
would be the most suitable type of training set? Would one use an
mixture of known "normal" inputs and randomly generated "abnormal"
inputs, with one output being a normal/abnormal indicator?

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