| Summary: | Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (<i>SGPNN</i>) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (<i>ANN</i>) to discover the hidden relation between the subgroups’ multi-labels. The <i>SGPNN</i> is a feedforward (<i>FF</i>), partially connected network that has a single middle layer and uses stairstep (<i>SS</i>) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed <i>SGPNN</i> consists of a multi-activation function neuron (<i>MAFN</i>) in the middle layer to rank each subgroup independently. The <i>SGPNN</i> uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single <i>SS</i> function. The proposed <i>SGPNN</i> using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed <i>SGPNN</i> achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.
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