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The quality of an immune-based negative selection algorithm hardly depends on quality of generated detectors. First, they should cover a nonself space in sufficient degree to guarantee high detection rates. Second, the duration of classification is proportional to the cardinality of detector's set. A time reaction for anomalies is especially important in on-line classification systems, e.g. spam and intrusion detection systems. Therefore, detectors should be sufficiently general (to reduce their number), as well as sufficiently specific (to detect many intruders). In this paper, we present an improved approach using double, real-valued and binary, detectors, designed to meet above stated requirements. We consider two version of proposed algorithms, which differs from each other at the degree of allowed overlapping regions. However, what is confirmed by presented experiments, too aggressive minimization of overlapping areas can be, not only computationally complex, but it provides lower detection rates also.