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Multitask Learning is a learning paradigm that deals with multiple differenttasks in parallel and transfers knowledge among them. XOF, a LearningClassifier System using tree-based programs to encode building blocks(meta-features), constructs and collects features with rich discriminativeinformation for classification tasks in an observed list. This paper seeks tofacilitate the automation of feature transferring in between tasks by utilisingthe observed list. We hypothesise that the best discriminative features of aclassification task carry its characteristics. Therefore, the relatednessbetween any two tasks can be estimated by comparing their most appropriatepatterns. We propose a multiple-XOF system, called mXOF, that can dynamicallyadapt feature transfer among XOFs. This system utilises the observed list toestimate the task relatedness. This method enables the automation oftransferring features. In terms of knowledge discovery, the resemblanceestimation provides insightful relations among multiple data. We experimentedmXOF on various scenarios, e.g. representative Hierarchical Boolean problems,classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,to validate its abilities of automatic knowledge-transfer and estimating taskrelatedness. Results show that mXOF can estimate the relatedness reasonablybetween multiple tasks to aid the learning performance with the dynamic featuretransferring.

Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks