Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries, which uses an m-bit array to represent a data set. Dynamic bloom filter (DBF) can support concisely representation and approximate membership queries of dynamic set instead of static set. It has been proved that DBF not only possess the advantage of standard bloom filter, but also has better features when dealing with dynamic set. But DBF also has a disadvantage: the addition operation which mapped element x into bloom filter s will become no sense, if some of the first s-1 bloom filters have already responded that element x is in set A with some false positive probability. We point out this shortcoming and improve the addition operation with a new algorithm. We call this improved dynamic bloom filter i-DBF. Finally, we prove that this i-DBF has better performance both in the storage space and in the false positive probability
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