干豆的聚类和分类具有不平衡的数据特征
Chou-Yuan Lee1, Wei Wang2, Jian-Qiong Huang3
1School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China. lqy@fzfu.edu.cn.
Scientific reports
|December 27, 2024
概括
本研究引入了一种新的算法,结合了边界线合成少数群体过量采样技术 (BLSMOTE) 和K-means集群,以提高不平衡数据集的机器学习分类准确性. 提出的方法显著改善了性能指标,如精度和回忆.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 传统的机器学习模型,如决策树 (DT),随机森林 (RF) 和支持矢量机 (SVM),在不平衡的数据集上表现出有限的分类性能.
- 不平衡的数据,其中一个类明显超过其他类,对模型训练和准确的预测构成挑战.
- 现有的方法往往难以有效地处理阶级不平衡,导致有偏见的模型和糟糕的概括.
研究的目的:
- 开发和评估一种新的混合算法,以提高不平衡数据集的分类准确性.
- 解决传统机器学习算法在处理不同类分布的数据集方面的局限性.
- 提高关键性能指标,如精度,回忆,F1得分和曲线下面积 (AUC).
主要方法:
- 拟议的算法集成了边界合成少数群体过量采样技术 (BLSMOTE) 与K-means集群.
- BLSMOTE在少数阶级的边界上生成合成样本,以减轻噪音和改善阶级代表性.
- K-意味着基于相似性的数据点进行聚类,进一步帮助数据分区和模型训练.
主要成果:
- 与传统方法相比,BLSMOTE + K-means + SVM算法在干豆和肥胖水平数据集上表现出优异的分类性能.
- BLSMOTE + K-means + DT成功地为两个数据集生成了决策规则,提供了可解释的见解.
- BLSMOTE + K-means + RF有效地对解释变量的重要性进行了排名,为特征选择提供了有价值的信息.
结论:
- 拟议的BLSMOTE + K-means混合方法为增强对不平衡数据的机器学习分类提供了强大的解决方案.
- 这种方法提高了整体预测准确度,并通过决策规则和变量重要性排名提供了有价值的见解.
- 这些发现提供了科学证据,以支持处理不平衡数据集的领域的决策过程.
更多相关视频
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
6.9K
09:21Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
Published on: July 7, 2023
1.4K
相关概念视频
01:29Aggregates Classification
289
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...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
289
01:11How Data are Classified: Categorical Data
30.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
30.8K
01:20Cluster Sampling Method
11.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.5K
01:26Classification of Systems-I
150
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:
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:
150
01:31Classification of Systems-II
119
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,
119
00:59How Data are Classified: Numerical Data
26.9K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
26.9K