Data mining integrates statistical analysis, machine learning and database technology to extract hidden patterns and relationships from data. The presence of irrelevant, redundant and inconsistent attributes in the data ushers poor classification accuracy. In this paper, a novel bio-inspired heuristic swarm optimization algorithm for feature selection, namely Constructive Lazy Wolf Search Algorithm is proposed based on the backbone of the Wolf Search Algorithm. It is based on the behavior of the real wolves, which search for their food and consequently survive the attacks of the threats by avoiding them. Based on the study conducted on the behavior of wolves two natural factors, namely laziness and health are introduced for attaining highest efficiency. Restricting and controlling the wolves’ behavior by allowing only healthy and constructive lazy wolves to take part in the search reduces the search time and complexity required to search for the best fitness. The proposed algorithm is then applied on a prisoner dataset for crime propensity prediction along with a few benchmark datasets to prove the stability in the improved performance compared with other bio-inspired optimization algorithms. The accuracy achieved by fine-tuning the proposed algorithm was 98.19% providing accurate crime prevention.

中文翻译：

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分类中特征选择的构造性惰性狼搜索算法

数据挖掘集成了统计分析、机器学习和数据库技术，从数据中提取隐藏的模式和关系。数据中存在不相关、冗余和不一致的属性会导致分类准确性较差。在本文中，基于狼搜索算法的主干，提出了一种新颖的用于特征选择的仿生启发式群优化算法，即构造惰性狼搜索算法。它基于真正的狼的行为，它们寻找食物并因此通过避开威胁而幸免于难。基于对狼的行为进行的研究，引入了两个自然因素，即懒惰和健康，以实现最高效率。通过只允许健康和有建设性的懒惰狼参与搜索来限制和控制狼的行为，从而减少了搜索最佳适应度所需的搜索时间和复杂性。然后将所提出的算法应用于犯罪倾向预测的囚犯数据集以及一些基准数据集，以证明与其他仿生优化算法相比改进性能的稳定性。通过微调所提出的算法实现的准确性为 98.19%，从而提供了准确的犯罪预防。然后将所提出的算法应用于犯罪倾向预测的囚犯数据集以及一些基准数据集，以证明与其他仿生优化算法相比改进性能的稳定性。通过微调所提出的算法实现的准确性为 98.19%，从而提供了准确的犯罪预防。然后将所提出的算法应用于犯罪倾向预测的囚犯数据集以及一些基准数据集，以证明与其他仿生优化算法相比改进性能的稳定性。通过微调所提出的算法实现的准确性为 98.19%，从而提供了准确的犯罪预防。