Abstract
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 (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN 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 SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN 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.
Highlights
Preference learning (PL) is an extended paradigm in machine learning that induces predictive ranking models from experimental data [1,2,3]
This paper introduces a simple network with one middle layer and a new activation function to speed up the learning to rank using the new Spearman objective function
This paper introduces the novel multi-activation function neuron (MAFN) to serve more than one group of labels
Summary
Preference learning (PL) is an extended paradigm in machine learning that induces predictive ranking models from experimental data [1,2,3]. Real-world data can be ambiguous and often lack preference relations between two or more labels, and the missing relations can be mapped to an indifference ∼, or incomparability ⊥, relation [8,9] These two relations create a partial order on the ω space where λa⊥λb or λa ∼ λb. Sometimes the data collected from the likes of recommender systems, elections, and surveys deviate from the population and in such cases label ranking cannot be predicted using the same learning model. Such a deviation is addressed by extracting patterns to identify the subgroup of data for the interesting targets using subgroup discovery (SD) approaches [12]. The approach of collecting the data from multiple sources processed by an expert system to be classified by MLP is proposed by Vincent, D. [20] to evaluate agricultural lands suitability
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