Identification of phytoplankton from flow cytometry data by using radial basis function neural networks

Abstract

We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91. 5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from "novel" species (species not present in the training data) were analyzed.

FIG. 1

Schematic diagram of an RBF neural network classifier. Raw data are distributed from the input layer via a “hidden” layer of processing units or nodes to an “output layer” where the network’s decision is formed. The bias node has a constant output value irrespective of input: its use allows output layer nodes to add a constant offset.

FIG. 2

Effect of basis function width, shape, and placement strategy on the proportion of test data patterns for 34 species that were identified correctly. (a) Effect of basis function width, for radially symmetric basis functions. Basis function centers were a randomly selected subset of the training data. Curves for four different network sizes are shown: 34 HLNs (■), 68 HLNs (▴), 102 HLNs (▵), and 136 HLNs (○). (b and c) Effect of basis function center selection strategy for radially symmetric basis functions (b) and non-radially symmetric basis functions (c) (formed by using the Mahalanobis distance). Curves for two network sizes (34 HLNs [open symbols] and 136 HLNs [closed symbols]) are shown for three selection strategies: random selection (squares), random selection followed by K-means unsupervised clustering (inverted triangles), random selection followed by LVQ supervised clustering (triangles).

FIG. 3

Use of a threshold parameter θ as a constraint on the summed output of all HLNs (a) and the maximum HLN output value (b) to reject data from “novel” species (not present in the training data). The proportion of test data patterns failing to satisfy the constraint, and therefore rejected as unknown, is shown for the 20 trained species (○) and the 14 novel species (▴).