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Found 8 papers, 1 papers with code
Provable Tensor Completion with Graph Information
For the model, we establish a rigorous mathematical representation of the dynamic graph, based on which we derive a new tensor-oriented graph smoothness regularization.
Efficient Fraud Detection Using Deep Boosting Decision Trees
In this paper, we propose deep boosting decision trees (DBDT), a novel approach for fraud detection based on gradient boosting and neural networks.
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration
Frequent Directions, as a deterministic matrix sketching technique, has been proposed for tackling low-rank approximation problems.
An interpretable neural network model through piecewise linear approximation
To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component.
Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis.
A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples
We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives.
A hybrid machine learning framework for analyzing human decision making through learning preferences
To meet the decision maker's demand for more interpretable machine learning models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding, which combines an additive value model and a fully-connected multilayer perceptron (MLP) to achieve good performance while capturing the explicit relationships between individual attributes and the prediction.
Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria
We also propose a few novel methods for classifying non-reference alternatives in order to enhance the applicability of our approach to different datasets.
