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Found 34 papers, 13 papers with code
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
The first idea behind the combination is to form constraints for a joint probability distribution over all combinations of concept values to satisfy the expert rules.
Generating Survival Interpretable Trajectories and Data
A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed.
Dual feature-based and example-based explanation methods
A new approach to the local and global explanation is proposed.
SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models
A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed.
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator
For every generated example, the survival function of the black-box model is computed, and the survival function of the surrogate model (the Beran estimator) is constructed as a function of the explanation coefficients.
A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints
A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed.
Neural Attention Forests: Transformer-Based Forest Improvement
The main idea behind the proposed NAF model is to introduce the attention mechanism into the random forest by assigning attention weights calculated by neural networks of a specific form to data in leaves of decision trees and to the random forest itself in the framework of the Nadaraya-Watson kernel regression.
Interpretable Ensembles of Hyper-Rectangles as Base Models
A new extremely simple ensemble-based model with the uniformly generated axis-parallel hyper-rectangles as base models (HRBM) is proposed.
Multiple Instance Learning with Trainable Decision Tree Ensembles
The whole STE-MIL model, including soft decision trees, neural networks, the attention mechanism and a classifier, is trained in an end-to-end manner.
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form called the neural kernels.
LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models
The first idea behind the models is to introduce a two-level attention, where one of the levels is the "leaf" attention and the attention mechanism is applied to every leaf of trees.
Improved Anomaly Detection by Using the Attention-Based Isolation Forest
A new modification of Isolation Forest called Attention-Based Isolation Forest (ABIForest) for solving the anomaly detection problem is proposed.
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression
The network is trained on controls, and it replaces standard kernels with a set of neural subnetworks with shared parameters such that every subnetwork implements the trainable kernel, but the whole network implements the Nadaraya-Watson estimator.
Attention and Self-Attention in Random Forests
New models of random forests jointly using the attention and self-attention mechanisms are proposed for solving the regression problem.
Attention-based Random Forest and Contamination Model
A new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classification are proposed.
Multi-Attention Multiple Instance Learning
In the method, one of the attention modules takes into account adjacent patches or instances, several attention modules are used to get a diverse feature representation of patches, and one attention module is used to unite different feature representations to provide an accurate classification of each patch (instance) and the whole bag.
Attention-like feature explanation for tabular data
The first part is a set of the one-feature neural subnetworks which aim to get a specific representation for every feature in the form of a basis of shape functions.
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data
One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP).
SurvNAM: The machine learning survival model explanation
The proposed method SurvNAM allows performing the local and global explanation.
Ensembles of Random SHAPs
According to the first modification, called ER-SHAP, several features are randomly selected many times from the feature set, and Shapley values for the features are computed by means of "small" SHAPs.
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
In contrast to the neural additive model, the method provides weights of features in the explicit form, and it is simply trained.
A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines
The main idea behind the approach is to use the stacking algorithm in order to learn a second-level meta-model which can be regarded as a model for implementing various ensembles of gradient boosting models.
Counterfactual explanation of machine learning survival models
A method for counterfactual explanation of machine learning survival models is proposed.
Gradient boosting machine with partially randomized decision trees
The gradient boosting machine is a powerful ensemble-based machine learning method for solving regression problems.
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
A new modification of the explanation method SurvLIME called SurvLIME-Inf for explaining machine learning survival models is proposed.
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov-Smirnov bounds.
SurvLIME: A method for explaining machine learning survival models
A new method called SurvLIME for explaining machine learning survival models is proposed.
An explanation method for Siamese neural networks
A new method for explaining the Siamese neural network is proposed.
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors
A new meta-algorithm for estimating the conditional average treatment effects is proposed in the paper.
An Adaptive Weighted Deep Forest Classifier
The idea underlying the modification is very simple and stems from the confidence screening mechanism idea proposed by Pang et al. to simplify the Deep Forest classifier by means of updating the training set at each level in accordance with the classification accuracy of every training instance.
A simple genome-wide association study algorithm
A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed.
Discriminative Metric Learning with Deep Forest
A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper.
