增强阿里巴巴和四十贼算法的特征选择。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Malik Braik
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引用次数: 10

摘要

特征选择(Feature Selection, FS)旨在通过从初始特征范围中选择一小部分合适的特征来提高数据集模型的分类率。因此,需要一种可靠的优化方法来处理这一问题所涉及的问题。传统方法往往不能对复杂数据集的高维特征空间进行最优降维,导致分类模型较弱。元启发式方法可以为高维数据集提供良好的分类率。在这里,一个名为阿里巴巴和四十大盗(AFT)的基于人类的新算法的二进制版本被应用于解决FS问题池。虽然AFT是一种有效的元启发式算法,但它有时会出现过早收敛和搜索性能低下的问题。通过提出三个增强版本的AFT,这些问题得到了缓解,即:(1)使用分层和分布式框架的二进制多层AFT,称为BMAFT,(2)使用精英学习策略的二进制精英AFT (BEAFT),以及(3)使用自适应跟踪距离参数的二进制自适应AFT (BSAFT)。这些版本以及基本二进制AFT (BAFT)对从不同存储库收集的24个问题进行了广泛的评估。结果表明,本文提出的算法在收敛速度和求解精度方面都有显著提高。最重要的是,总体结果表明BMAFT是最具竞争力的,与其他竞争算法相比,BMAFT提供了最好的结果,性能分数优异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhanced Ali Baba and the forty thieves algorithm for feature selection.

Enhanced Ali Baba and the forty thieves algorithm for feature selection.

Enhanced Ali Baba and the forty thieves algorithm for feature selection.

Enhanced Ali Baba and the forty thieves algorithm for feature selection.

Feature Selection (FS) aims to ameliorate the classification rate of dataset models by selecting only a small set of appropriate features from the initial range of features. In consequence, a reliable optimization method is needed to deal with the matters involved in this problem. Often, traditional methods fail to optimally reduce the high dimensionality of the feature space of complex datasets, which lead to the elicitation of weak classification models. Meta-heuristics can offer a favorable classification rate for high-dimensional datasets. Here, a binary version of a new human-based algorithm named Ali Baba and the Forty Thieves (AFT) was applied to tackle a pool of FS problems. Although AFT is an efficient meta-heuristic for optimizing many problems, it sometimes exhibits premature convergence and low search performance. These issues were mitigated by proposing three enhanced versions of AFT, namely: (1) A Binary Multi-layered AFT called BMAFT which uses hierarchical and distributed frameworks, (2) Binary Elitist AFT (BEAFT) which uses an elitist learning strategy, and, (3) Binary Self-adaptive AFT (BSAFT) which uses an adapted tracking distance parameter. These versions along with the basic Binary AFT (BAFT) were expansively assessed on twenty-four problems gathered from different repositories. The results showed that the proposed algorithms substantially enhance the performance of BAFT in terms of convergence speed and solution accuracy. On top of that, the overall results showed that BMAFT is the most competitive, which provided the best results with excellent performance scores compared to other competing algorithms.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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