Artificial Neural Network with unbalanced weights

Question

I have been reading the concept of ANN for applying it on my project (credit card fraud detection). Given a set of inputs to the network, say:

• A1 - Time to input PIN
• A2 - Amount to be withdrawn
• A3 - ATM location
• A4 - Global behavior (Time & date, & sequence in performing a transaction )

The more any of these inputs deviates from the "norm", the greater the weight of that input to the network. Here comes my question, how does the Neural Network treat a situation whereby one input's weight, say A1, is high whilst all the other weights are low?

Answer 1

The input probability density functions combine to form a multidimensional probability distribution (usually an ellipsoid in that many dimensions). The combination of the inputs is a vector, and the probability value at that point in the N-space tells you how likely it is to be real or fake. This works along each of the axes, where all but one input would be zero, as well as out where all the variables have significant values. If all of your inputs have smooth gaussian probability distributions your resulting probability distribution is a hyperellipsoid and you don't really need a neural net.

Using a neural net gets economical when you have a complicated probability density in one or more of the variables, or if combining the variables creates unexpected features (holes and bumps) in the probability density. Then the training of the neural net over a large number of real input combinations and known results tells it what regions of the input space are interesting and what regions are mundane. Again, you could just map them yourself in a big N-dimensional array with high resolution, if you have enough memory, but where's the fun in that? The neural net will also interpolate smoothly between regions, which may make its decisions more fuzzy than the actual probability space (ie, that's where the accuracy metric drops below 100%).