Abstract:
Human concept learning is semi-supervised learning (SSL) because people naturally combine a few labeled data with lots of unlabeled data to make classification decisions. Human concept learning is both static and dynamic. To accommodate new data, classical SSL algorithms must be redesigned. Concept-cognitive learning, which mimics human cognition, can implement dynamic processes. Many SSL methods were designed using instance feature vector information, ignoring concept structural information, which is crucial to human knowledge organization. Based on this idea, a new SSL method, semi-supervised concept learning method (S2CL), is proposed for dynamic SSL using concept spaces, which represent knowledge as hierarchical concept structures. We also propose S2CLα for concept learning to maximize global and local conceptual information. This paper first introduces new theories for S2CL (or S2CLα) based on a regular formal decision context, then designs a new SSL framework and its algorithm to effectively exploit unlabeled data. Finally, we test our methods, including concept classification and incremental learning on large amounts of unlabeled data, on various datasets.
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