用于提升不平衡遗传数据分类与回归性能的数据预处理方法、特征选择技术与机器学习模型的对比分析
Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data
AI总结：
本研究针对遗传数据不平衡、高基数等问题，对比分析不同预处理、特征选择与机器学习模型组合的性能，发现随机森林最适配不平衡回归任务，相关结论可推广至同类数据集。
摘要：
基因组测序技术的飞速发展催生了海量基因组数据的收集工作。研究人员可能希望在这类数据上应用机器学习模型，以预测基因突变的致病性或临床意义。然而，许多遗传数据集存在目标变量不平衡的问题，给机器学习模型带来了挑战：回归任务中观测值存在偏态/不平衡，分类任务中则存在类别不平衡问题。遗传数据集通常还具有高基数特性，且包含偏态的预测变量，这进一步加剧了挑战。我们旨在探究数据预处理、特征选择技术与模型选择对在这类数据集上训练的模型性能的影响。我们采用5折交叉验证衡量性能，并对比了不同技术组合下的平均r平方（r-squared）与准确率指标。研究发现，预测变量或目标变量中的异常值/偏态并未对回归模型构成挑战。我们还发现，类别不平衡的目标变量与偏态预测变量对分类性能几乎没有影响。随机森林（Random forest）是处理不平衡回归任务的最优模型。尽管本研究以遗传数据集作为实际应用案例，但我们的研究结论可推广至任何类似的数据集。
英文摘要：
Rapid advancements in genome sequencing have led to the collection of vast amounts of genomics data. Researchers may be interested in using machine learning models on such data to predict the pathogenicity or clinical significance of a genetic mutation. However, many genetic datasets contain imbalanced target variables that pose challenges to machine learning models: observations are skewed/imbalanced in regression tasks or class-imbalanced in classification tasks. Genetic datasets are also often high-cardinal and contain skewed predictor variables, which poses further challenges. We aimed to investigate the effects of data preprocessing, feature selection techniques, and model selection on the performance of models trained on these datasets. We measured performance with 5-fold cross-validation and compared averaged r-squared and accuracy metrics across different combinations of techniques. We found that outliers/skew in predictor or target variables did not pose a challenge to regression models. We also found that class-imbalanced target variables and skewed predictors had little to no impact on classification performance. Random forest was the best model to use for imbalanced regression tasks. While our study uses a genetic dataset as an example of a real-world application, our findings can be generalized to any similar datasets.
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