Feature-based Rule Research Articles

Semantic Deep Learning Integrated with RGB Feature-Based Rule Optimization for Facility Surface Corrosion Detection and Evaluation

AbstractOver the last few years, convolutional neural networks (CNNs) have been applied to detect corrosion in images. Unfortunately, the corrosion is detected in bounding boxes, without precisely ...

Detecting Malicious Domain Names with Abnormal WHOIS Records Using Feature-Based Rules

Abstract Millions of new domain names are registered every day, but a large proportion of them are malicious and usually discovered and blacklisted after the crime has been committed. In order to improve the security of domain name registration, this paper proposes a lightweight detection method based on the AdaBoost to identify malicious domain names, which focuses on proactively detecting malicious domain names by exploring the abnormal WHOIS records. The domain name registries and registrars can adopt the proposed method as the first layer of defense to identify malicious domains on the domain registration stage. Extensive experiments on a large-scale database demonstrate that the proposed approach achieves satisfactory results on various malicious domain names.

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

Article history: Received 30 April 2011 Received in revised form September, 01, 2011 Accepted 01 September 2011 Available online 2 September 2011 Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance. © 2012 Growing Science Ltd. All rights reserved

Role of Certainty Factor in Generating Rough-Fuzzy Rule

The generation of effective feature-based rules is essential to the development of any intelligent system. This paper presents an approach that integrates a powerful fuzzy rule generation algorithm with a rough set-assisted feature reduction method to generate diagnostic rule with a certainty factor. Certainty factor of each rule is calculated by considering both the membership value of each linguistic term introduced at time of fuzzyfication of data as well as possibility values, due to inconsistent data, generated by rough set theory at time of rule generation. In time of knowledge inferencing in an intelligent system, certainty factor of each rule will play an important role to find out the appropriate rule to be selected. Experimental results demonstrate the superiority of our approach.

Feature-based rule induction in machining operation using rough set theory for quality assurance

There have been many studies, mainly by the use of statistical modeling techniques, as to predicting quality characteristics in machining operations where a large number of process variables need to be considered. In conventional metal removal processes, however, an exact prediction of surface roughness is not possible or very difficult to achieve, due to the stochastic nature of machining processes. In this paper, a novel approach is proposed to solve the quality assurance problem in predicting the acceptance of computer numerical control (CNC) machined parts, rather than focusing on the prediction of precise surface roughness values. One of the data mining techniques, called rough set theory, is applied to derive rules for the process variables that contribute to the surface roughness. The proposed rule-composing algorithm and rule-validation procedure have been tested with the historical data the company has collected over the years. The results indicate a higher accuracy over the statistical approaches in terms of predicting acceptance level of surface roughness.

Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening

An approach to detecting landmines using ground-penetrating radar (GPR) based on feature-based rules, order statistics, and adaptive whitening (FROSAW) is described. FROSAW relies on independent adaptation of whitening statistics in different depths and combining feature-based methods with anomaly detection using rules. Constant false alarm rate (CFAR) detectors are used for anomaly detection on the depth-dependent adaptively whitened data. A single CFAR confidence measure is computed via a function of order statistics. Anomalies are detected at locations with high CFAR confidence. Depth-dependent features are computed on regions containing anomalies. Rules based on the features are used to reject alarms that do not exhibit mine-like properties. The utility of combining the CFAR and feature-based methods is evaluated. The algorithms and analysis are applied to data acquired from tens of thousands of square meters from several outdoor test sites with a state-of-the-art array of GPR sensors.

Feature-Based versus Exemplar-Based Strategies in Preschoolers′ Category Learning

Five experiments examined whether preschool children learn categories by formulating a categorization rule based on attention to one or two features, or by memorizing the category assignment for whole exemplars. Using fictitious animals as stimuli, children were taught a two-category discrimination at the basic level and were tested for their category learning using a procedure that required them to categorize from memory, rather than perform perceptual matching. Transfer stimuli manipulated the retention of features encountered in the training instances, and children were instructed to classify only the items about whose category membership they were certain. By measuring the extent of generalization, in addition to categorization accuracy, it was possible to assess whether children were applying a feature-based rule in making their categorization decisions. The studies found little evidence that children were employing rules. Analyses of the individual response patterns identified few children who categorized by attending to a single attribute such as shape, or else who used a two-feature disjunctive rule. In addition, a recognition memory test given after category training produced strong evidence that children had encoded the training examples as wholes, rather than focusing on individual features. Despite clear evidence that children had encoded the training examples, however, exemplar information was not used successfully in categorization when subjects were trained on only a single instance per category. The implications of these results for theories of early categorization are discussed.