A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of t...

In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction p...

We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under our approach, manifolds in high-dimensional spaces are inferred by estimating geometric relationships among the input instances. Unlike conventional manifold learning, we do not perform dimensionality reduction, but i...

Increasingly more clinicians use web Information Retrieval (IR) systems to assist them in diagnosing di cult medical cases, for instance rare diseases that they may not be familiar with. However, web IR systems are not necessarily optimised for this task. For instance, clinicians’ queries tend to be long lists of symptoms, often containing phrases, whereas web IR systems typically expect very s...

IR-drop problem is becoming more and more important. Previous works dealing with power/ground (P/G) network peak current reduction to reduce the IR-drop problem only focus on synchronous sequential logic circuits which consider the combinational parts as unchangeable [4],[5]. However, some large combinational circuits which work alone in one clock cycle can create large current peaks and induce...

The label learning mechanism is challenging to integrate into the training model of multi-label feature space dimensionality reduction problem, making current methods primarily supervision modes. Many only focus attention on correlations and ignore instance interrelations between original low dimensional space. Additionally, very few techniques consider how constrain projection matrix identify ...

We present a green and scalable route toward the formation of reduced graphene oxide (r-GO) by photothermal reduction induced by infrared (IR) irradiation, utilizing a bathroom IR lamp as the source of IR light. Thermogravimetric analysis, Raman, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy confirm the reduction of r-GO by IR light. Ultraviolet-visible-infrared s...

We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34% 43% improvement in recall for candidate triage for QA.