This is an implementation of the improved sticky binary PSO (ISBPSO) algorithm for feature selection (FS) for high-dimensional data. ISBPSO uses a new initialization strategy using the feature weighting information based on mutual information, and uses a dynamic bits masking strategy for gradually reducing the search space during the evolutionary process. For a detailed description of the method please refer to

Li, A.-D., Xue, B., & Zhang, M. (2021). Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies. Applied Soft Computing, 106, 107302. https://doi.org/10.1016/j.asoc.2021.107302 [BibTeX] [PDF]

The source code is in the src folder. An illustration to run the algorithm is implemented in ./ISBPSO/src/fs/RunPSO.java. Please note that the code requires the jar file (./ISBPSO/lib/weka.jar) of Weka, Machine Learning Software in Java. Please make sure it is added in the libiray. read me