NICE: an algorithm for nearest instance counterfactual explanations

An efficient instance selection algorithm for k nearest neighbor regression

The k-Nearest Neighbor algorithm(kNN) is an algorithm that is very simple to understand for classification or regression. It is also a lazy algorithm that does not use the training data points to do any generalization, in other words, it keeps all the training data during the testing phase. Thus, the population size becomes a major concern for kNN, since large population size may result in slow...

Nearest-Instance-Centroid-Estimation Linear Discriminant Analysis (NICE LDA)

We propose a novel ensemble classification technique called the Nearest Instance Centroid Estimation (NICE) LDA algorithm. Our algorithm (inspired from NICE KLMS) performs a combination of two weak classifiers threshold based clustering and linear discriminant classification to achieve stateof-the-art results on various high dimensional UCI datasets. We discuss the important ways in which our m...

An Instance Selection Algorithm Based on Reverse Nearest Neighbor

Explanations of Counterfactual Inferences

When engaging in counterfactual thought, people must imagine changes to the actual state of the world. In this study, we investigated how people reason about counterfactual scenarios by asking participants to make counterfactual inferences about a series of causal devices (i.e., answer questions such as If component X had not operated [had failed], would components Y, Z, and W have operated?) a...

An Algorithm for Predicting Recurrence of Breast Cancer Using Genetic Algorithm and Nearest Neighbor Algorithm

Introduction: Breast cancer is one of the most common types of cancer and the most common type of malignancy in women, which has been growing in recent years. Patients with this disease have a chance of recurrence. Many factors reduce or increase this probability. Data mining is one of the methods used to detect or anticipate cancers, and one of its most common uses is to predict the recurrence...