Search Results for author:
Found 11 papers, 6 papers with code
Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps
In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification.
Which is the best model for my data?
In this paper, we tackle the problem of selecting the optimal model for a given structured pattern classification dataset.
Multi-objective hyperparameter optimization with performance uncertainty
The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters.
Forward Composition Propagation for Explainable Neural Reasoning
In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron.
Modeling Implicit Bias with Fuzzy Cognitive Maps
This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete.
Prolog-based agnostic explanation module for structured pattern classification
Thirdly, we encode instances as a Prolog rule using the nominal values, the predefined symbols, the decision classes, and the confidence values.
Measuring Wind Turbine Health Using Drifting Concepts
In this paper, we propose two new approaches for the analysis of wind turbine health.
A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets
The need to measure bias encoded in tabular data that are used to solve pattern recognition problems is widely recognized by academia, legislators and enterprises alike.
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters.
Online learning of windmill time series using Long Short-term Cognitive Networks
Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling.
Long Short-term Cognitive Networks
In this paper, we present a recurrent neural system named Long Short-term Cognitive Networks (LSTCNs) as a generalization of the Short-term Cognitive Network (STCN) model.
