The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments -the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life

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... aset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.