Scholarlink

<p>The efficiency of multi-objective soft subspace clustering algorithms (MSSCAs) can be low when applied to large-scale datasets. This inefficiency arises because the multi-objective evolutionary algorithms (MOEAs) utilized in MSSCAs often require a large number of soft subspace clustering objective function evaluations due to their population-based nature. Moreover, relying solely on negative Shannon entropy to constrain feature weights is inadequate for soft subspace clustering algorithms. To address these issues, a knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering (KCRS-MOSSC) algorithm is presented. First, an inter-cluster feature weight dissimilarity function is designed to further constrain the feature weights. Furthermore, a novel surrogate-based optimization framework called the knowledge-guided classification and regression surrogates co-assisted multi-objective evolutionary framework (KCRS-MOEF) is proposed to efficiently optimize the proposed inter-cluster feature weight dissimilarity function, intra-cluster compactness function, inter-cluster separation function, and negative Shannon entropy function. In KCRS-MOEF, a classification decision tree is utilized as the classification surrogate model to help generate a set of promising offspring, while a radial basis function (RBF) model is employed as the regression surrogate model to assist in the infill criterion by predicting the objective function values of the offspring. Furthermore, to fully leverage the knowledge of the evolutionary process, an infill criterion guided by dynamic process knowledge of elite individuals is designed to enhance the convergence and diversity of the population. Finally, a clustering ensemble strategy based on knee point guidance is proposed to generate a final solution from a set of non-dominated individuals. KCRS-MOEF outperforms state-of-the-art counterparts in terms of convergence, diversity, and time efficiency, as demonstrated in four experiments conducted on the DTLZ benchmark. Furthermore, experiments on various datasets show that the clustering performance and time efficiency of KCRS-MOSSC exceed those of comparison algorithms.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering algorithm

  • 期刊论文

  • 发表日期：

    2025年2月15日

查看全文
  • Feng Zhao,
  • Lu Li,
  • Hanqiang Liu

摘要

The efficiency of multi-objective soft subspace clustering algorithms (MSSCAs) can be low when applied to large-scale datasets. This inefficiency arises because the multi-objective evolutionary algorithms (MOEAs) utilized in MSSCAs often require a large number of soft subspace clustering objective function evaluations due to their population-based nature. Moreover, relying solely on negative Shannon entropy to constrain feature weights is inadequate for soft subspace clustering algorithms. To address these issues, a knowledge-guided classification and regression surrogates co-assisted multi-objective soft subspace clustering (KCRS-MOSSC) algorithm is presented. First, an inter-cluster feature weight dissimilarity function is designed to further constrain the feature weights. Furthermore, a novel surrogate-based optimization framework called the knowledge-guided classification and regression surrogates co-assisted multi-objective evolutionary framework (KCRS-MOEF) is proposed to efficiently optimize the proposed inter-cluster feature weight dissimilarity function, intra-cluster compactness function, inter-cluster separation function, and negative Shannon entropy function. In KCRS-MOEF, a classification decision tree is utilized as the classification surrogate model to help generate a set of promising offspring, while a radial basis function (RBF) model is employed as the regression surrogate model to assist in the infill criterion by predicting the objective function values of the offspring. Furthermore, to fully leverage the knowledge of the evolutionary process, an infill criterion guided by dynamic process knowledge of elite individuals is designed to enhance the convergence and diversity of the population. Finally, a clustering ensemble strategy based on knee point guidance is proposed to generate a final solution from a set of non-dominated individuals. KCRS-MOEF outperforms state-of-the-art counterparts in terms of convergence, diversity, and time efficiency, as demonstrated in four experiments conducted on the DTLZ benchmark. Furthermore, experiments on various datasets show that the clustering performance and time efficiency of KCRS-MOSSC exceed those of comparison algorithms.