What is cost sensitive classification?
Synonyms. Learning with different classification costs, cost-sensitive classification. Definition. Cost-Sensitive Learning is a type of learning in data mining that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost.
Is XGBoost cost sensitive?
The results indicated that the cost-sensitive XGBoost model had been skillful, and could improve classification accuracy in four datasets. In addition, this work evaluated the model performance by accuracy, ROC AUC, and k- Fold cross-validation to ensure that the new models is accurate.
What is cost sensitive neural network?
cost sensitive learning methods solve data imbalance problem based on the consideration of the cost associated with misclassifying samples. In particular, it assigns different cost values for the misclassification of the samples. — Training Deep Neural Networks on Imbalanced Data Sets, 2016.
What is the cost of misclassification?
In cost-sensitive learning instead of each instance being either correctly or incorrectly classified, each class (or instance) is given a misclassification cost. Thus, instead of trying to optimize the accuracy, the problem is then to minimize the total misclassification cost.
What are cost sensitive performance metric?
A new cost-sensitive metric is proposed to find the optimal tradeoff between the two most critical performance measures of a classification task – accuracy and cost. The proposed method accounts for an inherent ordinal data structure, total misclassification cost of a classifier, and imbalanced class distribution.
What is a cost matrix?
Definition. A Cost Matrix is a method for adjusting the weight assigned to misclassifications by Credit Scoring Models in particular supervised models. The cost matrix offers a means to differentiate the importance of Type I and Type II classification errors.
How does XGBoost work?
XGBoost minimizes a regularized (L1 and L2) objective function that combines a convex loss function (based on the difference between the predicted and target outputs) and a penalty term for model complexity (in other words, the regression tree functions).
Is XGBoost sensitive to class imbalance?
This modified version of XGBoost is referred to as Class Weighted XGBoost or Cost-Sensitive XGBoost and can offer better performance on binary classification problems with a severe class imbalance. In this tutorial, you will discover weighted XGBoost for imbalanced classification.
What is cost matrix in machine learning?
For machine-learning classification models, the cost matrix is the most common approach for reducing specific types of classification error (Fielding 2007). This matrix is an array of numbers organized in columns and rows, and each number specifies a cost for each outcome in the confusion matrix.
What is imbalanced classification?
Imbalanced classification is the problem of classification when there is an unequal distribution of classes in the training dataset. The imbalance in the class distribution may vary, but a severe imbalance is more challenging to model and may require specialized techniques.
What is a misclassification cost in decision tree?
For categorical (nominal, ordinal) dependent variables, misclassification costs allow you to include information about the relative penalty associated with incorrect classification.
What does misclassification mean?
Definition of misclassification : an act or instance of wrongly assigning someone or something to a group or category : incorrect classification Cracking down on the misclassification of workers so that those mislabeled as “independent contractors” can become unionizable employees.—
How to setup cost-sensitive evaluation in Weka?
In the WEKA Explorer, after loading my arff file I can setup a cost matrix from Classify->More Options…->Cost-sensitive evaluation->Set…->There is a 2×2 grid that appears in the weka cost-sensitive evaluation after I set the classes == 2
What is a cost sensitive metaclassifier?
A metaclassifier that makes its base classifier cost sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class).
How to minimize expected misclassification cost for training instances?
Valid options are: -M Minimize expected misclassification cost. Default is to reweight training instances according to costs per class -C File name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand.