Question
Hello I'm currently using neural network for classification of a dataset. Of course before doing classification either the data points or the features should be normalized. The toolbox which I'm using for neural network requires all values to be in range [0,1]. Does it make sense to first apply z-score and then to scale to range [0,1]? Second, should I normalize along the feature vectors or the data points (either applying z-score or to range [0,1])?
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Expert Answer
John Michell
PhD Expert
Answered Nov 20, 2025
It is well known (e.g., see the comp.ai.neural-nets FAQ) that the most efficient MLP nets are those which have
- 1. Bipolar sigmoid hidden node transfer functions, e.g., TANSIG( == TANH ), NOT LOGSIG !
- 2. Bipolar scaled input variables. For example
- a. Normalized to [-1,1] via MAPMINMAX (MATLAB's default)
- b. Standardized to zero-mean/unit-variance via MAPSTD or ZSCORE
- 3. However, the initial weight assignments should assure that initial hidden node outputs are in the linear region of the sigmoid.
Before training I always use the functions MINMAX (NOT mapminmax), ZSCORE and PLOT to eliminate or modify outliers and incorrect data.
Even though I prefer standardization, I accept MATLAB's [-1,1] default, which I assume is taken into account by MATLAB's default weight initialization. (I guess I should check this ... I've been burned by other logical assumptions).
BOTTOM LINE: Always use centered inputs and tansig hidden layer functions for MLPs. [If you don't, people may point at you and laugh (:>( ].
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