一个规范的直角激活反向学习神经网络用于回归和与异常值进行分类
Zhijun Zhang1, Yating Song1, Tao Chen1
1School of Automation Science and Engineering, South China University of Technology, China.
概括
一个新的神经网络,正规化的直角激活反向学习 (ROAIL),有效地减少了回归和分类中的异常影响. 威尔士-ROAIL变体显示出强大的异常值抑制能力与单步计算.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 异常值显著影响回归和分类模型的性能.
- 传统方法通常需要代计算,增加复杂性.
- 有效的异常值减少技术对于可靠的数据分析至关重要.
研究的目的:
- 引入一种新的规范化的直角激活反向学习 (ROAIL) 神经网络.
- 开发一种计算效率高的方法来减少异常影响.
- 在回归和分类任务中增强模型稳定性.
主要方法:
- 开发了一个单步计算ROAIL神经网络.
- 将扩展的 Gegenbauer 多项式扩展到一个多变量版本.
- 集成l2调节和威尔什损失函数,创建l2-ROAIL和W-ROAIL变体.
主要成果:
- l2-ROAIL最大限度地降低了经验和结构风险,提高了概括性.
- 通过使用Welsch函数,W-ROAIL通过向下加权异常值来提高稳定性.
- 在回归和分类中的实验证明了W-ROAIL强烈的异常值抑制.
结论:
- ROAIL提供了一种高效的,单步骤的方法来获得神经网络的重量.
- 与l2-ROAIL相比,W-ROAIL在异常值方面提供了更好的稳定性.
- 拟议的ROAIL框架有效地减少了机器学习模型中异常值的影响.
相关概念视频
Outliers and Influential Points
4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
What Are Outliers?
3.8K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.8K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Regression Analysis
5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Quantifying and Rejecting Outliers: The Grubbs Test
1.6K
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...
1.6K


