In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity.

中文翻译：

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通过使用模拟退火算法改进优化来增强特征选择，以减少入侵检测数据集的维数

在当前情况下，特征选择（FS）仍然是机器学习中非常重要的功能之一。减少特征集（FSt）有助于提高分类器的准确性。由于数据集 (DS) 中存在大量数据，因此从 DS 中选择所需特征仍然是一个庞大的过程。因此，为了解决这个问题，提出了一种新的基于混沌准对抗的火烈鸟搜索算法和模拟退火算法（CQOFSASAA），用于FS并从DS中选择最佳FSt，因此，这减少了DS'方面。FSA技术可用于从DS中选择最佳特征子集。广义环交叉也被用来从 DS 中选择非常相关的功能。最后，内核极限学习机 (KELM) 分类器对所选特征进行验证。所提供的范例的执行已经通过标准 DS 进行了测试，并且结果已与其余范例相关。实验结果证实，CQOFSASAA 的准确度为 93.74%，灵敏度为 92%，特异性为 92.1%。