A general insight into the effect of neuron structure on classification

Author:
, , , , ,
Year
: 2012
Abstract: This paper gives a general insight into how the neuron structure in a multilayer

perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the

common neuron structures are based on monotonic activation functions and linear input

mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation

function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these

high power neurons usually requires a less number of hidden nodes than conventional MLP

for solving classification problems. The fewer number of neurons is equivalent to the smaller

number of network weights that must be optimally determined by a learning algorithm. The

performance of learning algorithm is usually improved by reducing the number of weights,

i.e., the dimension of the search space. This usually helps the learning algorithm to escape

local optimums, and also, the convergence speed of the algorithm is increased regardless of

which algorithm is used for learning. Several 2-dimensional examples are provided manually

to visualize how the number of neurons can be reduced by choosing an appropriate neuron

structure. Moreover, to show the efficiency of the proposed scheme in solving real-world

classification problems, the Iris data classification problem is solved using an MLP whose

neurons are equipped by nonmonotonic activation functions, and the result is compared with

two well-known monotonic activation functions.
Keyword(s): Neuron structure

Nonmonotonic activation function

Nonlinear input

mapping

Classification

Multilayer perceptron (MLP)

Iris data classification
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