Confidence-based ant random walks

To facilitate the computer-aided medical applications, this paper tries to build better intelligent diagnosis systems with the help of swarm intelligence method. As to the clinical data, a built-in graph structure is constructed with training samples being mapped as labeled vertices and test samples being unlabeled vertices. On the basis of the iterative label propagation algorithm, this paper first introduces a confidence-based random walk learning model, where unlabeled vertices that consistently show high probability (above the confidence threshold) in belonging to one class is treated as labeled vertices in the next iteration. Later motivated by the swarm intelligence, this model is further improved by treating the labeled vertices as real ants in nature and the predefined classes as different ant colonies. A novel labeled ant random walk algorithm is introduced by incorporating the history information of random walk in the form of aggregation pheromone. The proposed algorithms are evaluated with a synthetic data as well as some real-life clinical cases in terms of diagnostic accuracy. Experimental results show the potentiality of the proposed algorithms.