Machine learningFinancial fraudMeta-ClassifiersVoting-ClassifierStacked-ClassifierWe develop Meta-Classifiers to detect financial frauds by combining several accurate and diverse stand-alone classifiers. Our results suggest that the Meta-Classifiers developed in our study can outperform the best stand-alone ...
Meta-Classifier: Meta-learning algorithm for classification predictive modeling tasks. Meta-Regression: Meta-learning algorithm for regression predictive modeling tasks.After a meta-learning algorithm is trained, it results in a meta-learning model, e.g. the specific rules, coefficients, or structure ...
适应性可以理解为,给定训练数据集训练的模型,却可以接近类似的不同的问题的能力。 换言之, 元学习是 让模型具有接近新task的能力, 也可以称之为learning to learn。 一下提供两个简单的例子来理解什么是元学习: 一个分类器(classifier)只在没有猫咪的图片集上进行了训练,但是输入一张猫咪的图片可以分辨此图是否...
The loss function directly computes the negative log-likelihood of the true class labels given the output probabilities from the classifier. The training and evaluation cycle of the model, ensuring consistency in learning and testing environments, is summarized in the Eq. 2, $$\begin{aligned} {...
Meta-Learning for Multi-label Classification 需要解决的一个不可微分的问题(threshold 这里有个对梯度的截断),所以文章提出用 Policy gradient 的方法来训练这个 classifier,设计了一个跟预测的 threshold 以及 prediction label prob 的相关的 reward,具体请参考文章中的公式,一句话说就是分类正确并且和预测的 label...
information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except ...
Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than
we used a sophisticated supervised machine learning classifier (SML): RandomForest Classifier (RFC)26and Gradient Boosting Classifier (GBC)27using built-in QIIME2. RFC is one of the most accurate for managing large and noisy datasets. This learning algorithm often manages unbalanced sample distributio...
user ("You are a sentiment classifier. For each message, give the percentage of positive/netural/negative."), user ("I liked it"), assistant ("70% positive 30% neutral 0% negative"), user ("It could be better"), assistant ("0% positive 50% neutral 50% negative"), ...
Defining fθ as a classifier, parameter θ extracts the probability of data points belonging to category y, which is given by the following attribute vector x, Pθ(y∣x). The optimal parameters should maximize the probability of identifying the true label in multiple training batches B ⊂ D...