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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

203
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
203
Classification of Systems-II01:31

Classification of Systems-II

163
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
163
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
505
Aggregates Classification01:29

Aggregates Classification

340
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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相关实验视频

Updated: Jul 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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通过使用基于PCA的特征选择技术,研究处理类失衡问题,使用机器学习方法来检测代码气味的严重程度.

Rajwant Singh Rao1, Seema Dewangan1, Alok Mishra2

  • 1Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.

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|September 27, 2023
PubMed
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此摘要是机器生成的。

这项研究有效地使用机器学习检测代码气味的严重性,实现高精度. 应用合成少数群体过量采样技术 (SMOTE) 和特征选择提高了模型性能,从而提高了软件的维护能力.

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相关实验视频

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科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 代码气味表明设计缺陷,增加维护成本和降低软件质量.
  • 高严重度的代码气味对系统的维护性构成重大挑战,需要对重构进行准确的严重性评估.
  • 阶级不平衡使精确的代码嗅觉严重性检测变得复杂.

研究的目的:

  • 使用机器学习技术来检测代码气味的严重性.
  • 为了解决代码气味数据集中的类失衡,使用合成少数群体过量采样技术 (SMOTE).
  • 评估特征选择和机器学习模型在代码嗅觉严重性分类中的有效性.

主要方法:

  • 使用了四个代码气味严重性数据集:数据类,上帝类,特征嫉妒和长方法.
  • 应用主要组件分析 (PCA) 用于特征选择和SMOTE用于类平衡.
  • 采用了五种机器学习模型:K-最近邻居,随机森林,决策树,多层感知器和物流回归.

主要成果:

  • 用随机森林和决策树模型为长方法代码气味获得了0.99的准确度.
  • 通过使用准确度,精度,回忆和F测量来评估模型性能.
  • 对比了SMOTE对模型性能的影响,证明了它的好处.

结论:

  • 开发的模型显示了准确的代码嗅觉严重性检测有希望的结果.
  • 该研究强调了解决阶级不平衡和使用特征选择以提高性能的重要性.
  • 这些发现可以指导未来的软件自动化质量评估和重构优先级的研究.