The deep lasso algorithm (dlasso) is introduced as a neural version of the statistical linear lasso algorithm that holds benefits from both methodologies: feature selection and automatic optimization of the parameters (including the regularization parameter). This last property makes dlasso particularly attractive for feature selection on small samples. In the two first conducted experiments, it was observed that dlasso is capable of obtaining better performance than its non-neuronal version (traditional lasso), in terms of predictive error and correct variable selection. Once that dlasso performance has been assessed, it is used to determine whether it is possible to predict the severity of symptoms in children with ADHD from four scales that measure family burden, family functioning, parental satisfaction, and parental mental health. Results show that dlasso is able to predict parents' assessment of the severity of their children's inattention from only eight items from the previous scales. These items are related to parents' satisfaction and degree of parental burden.

中文翻译：

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使用家庭负担信息准确预测儿童多动症严重程度：神经套索方法

深度套索算法（dlasso）作为统计线性套索算法的神经版本而引入，它从两种方法中受益：特征选择和参数（包括正则化参数）的自动优化。这最后一个属性使dlasso特别适合小样本上的特征选择。在两个首次进行的实验中，观察到在预测误差和正确变量选择方面，dlasso能够比其非神经元版本（传统lasso）获得更好的性能。一旦评估了dlasso的表现，就可以从四个衡量家庭负担，家庭功能，父母满意度和父母心理健康的量表中确定是否有可能预测多动症儿童的症状严重程度。结果表明，dlasso能够从以前的量表中仅通过8个项目来预测父母对孩子注意力不集中的严重程度的评估。这些项目与父母的满意度和父母负担的程度有关。