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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

167
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:
167
Classification of Systems-II01:31

Classification of Systems-II

132
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,
132
Classification of Signals01:30

Classification of Signals

369
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...
369
Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Aggregates Classification01:29

Aggregates Classification

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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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相关实验视频

Updated: May 22, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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多标签分类用于软件工程中的缺陷预测.

Jalaj Pachouly1, Swati Ahirrao2, Ketan Kotecha3,4

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, India.

Scientific reports
|March 14, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了软件缺陷预测的多标签分类,优于传统方法. 平衡数据集显著改善了机器学习和深度学习模型在缺陷报告中的性能.

关键词:
阶级不平衡造成的失衡数据预处理数据的预处理.缺陷预测的预测缺陷的预测功能选择 功能选择多标签分类的分类方式.软件工程 软件工程 软件工程

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科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 在软件开发中,缺陷预测至关重要.
  • 传统的缺陷预测使用多类分类,这是不够的,因为缺陷可以有多个标签.
  • 多标签分类为缺陷预测提供了更合适的方法.

研究的目的:

  • 为了调查软件缺陷的多标签性质.
  • 应用机器学习和深度学习技术用于多标签缺陷分类.
  • 在缺陷预测中解决类不平衡和标签相关性.

主要方法:

  • 将缺陷报告 (标题,内容,评论,代码片段) 的数据编译成整体总结.
  • 传统分类器的实施:多项天真贝叶斯,物流回归,随机森林.
  • 深度学习模型的应用:多层感知器 (MLP) 和具有分类链的卷积神经网络 (CNN).
  • 使用千平方测试选择特征和减少维度.
  • 使用非负最小平方 (NNLS) 处理类不平衡.

主要成果:

  • 在数据集平衡后,在机器学习和深度学习模型中观察到显著的性能改善.
  • 多标签分类的有效性通过像哈明损失,回忆,精度和F1分数这样的评估指标来证明.
  • 奇平方测试对于数据集质量评估和特征选择非常有用.

结论:

  • 多标签分类是一种更适合于软件缺陷预测的方法.
  • 数据集平衡对于提高缺陷预测模型性能至关重要.
  • 提出的方法为改善缺陷预测准确度提供了一个强大的框架.