School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.
NICE Research Group, School of Computer Science and Electronic Engineering, University of Surrey Guildford, GU2 7XS, UK.
Int J Neural Syst. 2024 Mar;34(3):2450014. doi: 10.1142/S012906572450014X. Epub 2024 Feb 9.
Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, (1996) 87-93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, (1997) 880-888; M. Aldwaik and H. Adeli, Advances in optimization of highrise building structures, (2014) 899-919; E. G. González, J. R. Villar, Q. Tan, J. Sedano and C. Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, (2022) 41-52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases.
特征选择（FS）因其在提高学习算法性能方面的作用而受到认可，特别是在高维数据集方面。最近，FS 被构建为一个多目标优化问题，导致各种多目标进化算法（MOEAs）被应用于解决该问题。然而，随着数据集维度的增加，解空间呈指数级扩展。同时，由于大量不相关和冗余特征的存在，广泛的搜索空间往往会导致许多局部最优解[H. Adeli 和 H. S. Park，通过大规模并行机器上的混合 AI 模型全自动设计超高摩天大楼结构，（1996）87-93]。因此，现有的 MOEAs 在局部最优停滞方面存在困难，特别是在大规模多目标 FS 问题（LSMOFSP）中。不同的 LSMOFSP 通常具有独特的特征，但大多数现有的 MOEAs 依赖于单一的候选解生成策略（CSGS），对于不同的 LSMOFSP 可能效率较低[H. S. Park 和 H. Adeli，用于优化大型钢结构的分布式神经动力学算法，（1997）880-888；M. Aldwaik 和 H. Adeli，高层建筑结构优化的进展，（2014）899-919；E. G. González、J. R. Villar、Q. Tan、J. Sedano 和 C. Chira，使用 A*和协同进化算法的高效多机器人路径规划解决方案，（2022）41-52]。此外，为特定的 LSMOFSP 选择合适的 MOEA 并确定其相应的参数值是耗时的。为了解决这些挑战，提出了一种多目标自适应粒子群优化（MOSaPSO）算法，结合了快速非支配排序方法。MOSaPSO 采用自适应机制，结合五种改进的高效 CSGS 生成新的解决方案。在十个数据集上进行了实验，结果表明，MOSaPSO 有效地减少了特征的数量，同时降低了分类错误率。此外，与同类算法相比，在训练集和测试集上的性能都有所提高，随着维度的增加，优势越来越明显。
