Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Associative Classification (AC) mining is a promising approach that uses association rules discovery techniques to construct association classifiers. Hover, traditional AC algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier.  introduces  AC-CS  , a novel AC algorithm, inspired by the clonal selection of the immune system. The algorithm proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on real datasets of different sizes show that the approach generates dramatically less rules than traditional AC algorithms. Hence, the proposed approach is indeed significantly more efficient than traditional AC algorithms while achieving good competitive accuracy.
['Samir A. Mohamed Elsayed', 'Sanguthevar Rajasekaran', 'R. Ammar']
I am writing a research paper on a idea An artificial immune system approach to associative classification
[{"from": "human", "value": "I am writing a research paper on a idea An artificial immune system approach to associative classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Associative Classification (AC) mining is a promising approach that uses association rules discovery techniques to construct association classifiers. Hover, traditional AC algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier.  introduces  AC-CS  , a novel AC algorithm, inspired by the clonal selection of the immune system. The algorithm proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on real datasets of different sizes show that the approach generates dramatically less rules than traditional AC algorithms. Hence, the proposed approach is indeed significantly more efficient than traditional AC algorithms while achieving good competitive accuracy."}]