About: Efficient classification methods can improve the data quality or relevance to better optimize some Internet applications such as fast searching engine and accurate identification. However, in the big data era, difficulties and volumes of data processing increase drastically. To decrease the huge computational cost, heuristic algorithms have been used. In this paper, an Adapting Chemotaxis Bacterial Foraging Optimization (ACBFO) algorithm is proposed based on basic Bacterial Foraging Optimization (BFO) algorithm. The aim of this work is to design a modified algorithm which is more suitable for data classification. The proposed algorithm has two updating strategies and one structural changing. First, the adapting chemotaxis step updating strategy is responsible to increase the flexibility of searching. Second, the feature subsets updating strategy better combines the proposed heuristic algorithm with the KNN classifier. Third, the nesting structure of BFO has been simplified to reduce the computation complexity. The ACBFO has been compared with BFO, BFOLIW and BPSO by testing on 12 widely used benchmark datasets. The result shows that ACBFO has a good ability of solving classification problems and gets higher accuracy than the other comparation algorithm.   Goto Sponge  NotDistinct  Permalink

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  • Efficient classification methods can improve the data quality or relevance to better optimize some Internet applications such as fast searching engine and accurate identification. However, in the big data era, difficulties and volumes of data processing increase drastically. To decrease the huge computational cost, heuristic algorithms have been used. In this paper, an Adapting Chemotaxis Bacterial Foraging Optimization (ACBFO) algorithm is proposed based on basic Bacterial Foraging Optimization (BFO) algorithm. The aim of this work is to design a modified algorithm which is more suitable for data classification. The proposed algorithm has two updating strategies and one structural changing. First, the adapting chemotaxis step updating strategy is responsible to increase the flexibility of searching. Second, the feature subsets updating strategy better combines the proposed heuristic algorithm with the KNN classifier. Third, the nesting structure of BFO has been simplified to reduce the computation complexity. The ACBFO has been compared with BFO, BFOLIW and BPSO by testing on 12 widely used benchmark datasets. The result shows that ACBFO has a good ability of solving classification problems and gets higher accuracy than the other comparation algorithm.
Subject
  • Algorithms
  • Classification algorithms
  • Distributed computing problems
  • Statistical classification
  • Mathematical logic
  • Theoretical computer science
  • Software architecture
  • Programming language topics
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