Extracting classification rules based on a cumulative probability distribution approach

This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules.The method includes two phases:(1) automatic generation of the membership function,and (2) use of the corresponding linguistic data to extract classification rules.The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics,and generate the fuzzy membership functions automatically.Experimental results show that the proposed method surpasses traditional methods in accuracy.

Urban Morphological Rules Classification for Digital Design

The hidden dimension of the urban morphology is the underlying the urban morphological rules system.The number of these rules has increased and their application tends to become more complex.The urban morphosis based digital approaches tends to become widespread.However,achieving the target values for all the rules is difficult.This impacts the social,environmental and aesthetic objectives of these rules.This paper proposes a classification of urban morphological rules to assist the digital morphosis of urban form.The aim is to endow the system of rules with a hierarchy,which can make efficient the automatic generation of the urban forms respectful of the urban law.Thus,this work promotes the concerns of artificial intelligence in urban morphology.

Correct Classification Rates in Multi-Category Discriminant Analysis of Spatial Gaussian Data

This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.

Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

Association rules’learning is a machine learning method used in finding underlying associations in large datasets.Whether intentionally or unintentionally present,noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy.This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier.Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning.This paper utilizes them to remove noise from the dataset before training the association rules classifier.Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques,namely:DecrementalReduction Optimization Procedure(DROP)3,DROP5,ALL K-Nearest Neighbors(ALLKNN),Edited Nearest Neighbor(ENN),and Repeated Edited Nearest Neighbor(RENN)in different noise ratios.Experiments show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels:0%,5%,and 10%.The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine(UCI)machine learning repository.The improvements were more apparent in the 5%and the 10%noise cases.When RENN was applied,the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47%to 76.65%compared to the original test.The average accuracy was improved from 66.08%to 77.47%for the 5%-noise case and from 59.89%to 77.59%in the 10%-noise case.Higher confidence was also reported in building the association rules when RENN was used.The above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier,especially in noisy domains.

Urdu Ligature Recognition System:An Evolutionary Approach

Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics.Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks,reporting exceptional results.The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition.Overall the proposed recognition system observes the processes of pre-processing,segmentation,feature extraction,hierarchical clustering,classification rules and genetic algorithm optimization and recognition.The pre-processing stage removes noise from the sentence images,whereas,in segmentation,the sentences are segmented into ligature components.Fifteen features are extracted from each of the segmented ligature images.Intra-feature hierarchical clustering is observed that results in clustered data.Next,classification rules are used for the representation of the clustered data.The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures.Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results,achieving a recognition rate of 96.72%.

A dynamic rule-induction method for classification in data mining

Rule induction(RI)produces classifiers containing simple yet effective‘If–Then’rules for decision makers.RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items(attribute values)in the training data instances.In response to the above two issues,a new dynamic rule induction(DRI)method is proposed.Whenever a rule is produced and its related training data instances are discarded,DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion.Therefore,the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM.This enables DRI to generate near perfect rules and realistic classifiers.Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.

保障医疗设备硬件系统功能安全的有效方法(英文)

A medical device is an instrument that includes components,parts,or accessories to diagnose or treat patients.Since the complexity of medical devices has increased in recent years,functional safety and basic safety are required to ensure the overall device safety.Functional safety is part of the overall safety that relates to the equipment under control(EUC)and to the EUC control system that depends on the correct functionality of the electrical/electronic/programmable electronic(E/E/PE)safety-related systems.This study proposes approach methods to functional safety of medical devices for which it is important to correctly identify the safety functions and the safety integrity level(SIL).The relationship between the functional safety and essential performance is identified focusing on the safety function.The essential performance of E/E/PE systems is defined as the safety function of the functional safety.The target SIL of the essential performance is determined according to the potential risk levels,based on the classification rules of medical devices.This approach is applied to the pulse oximeter as a case study.The target SIL for the functionality of the power-failure alarm condition is determined to be SIL1.The target SILs of other functions are determined as SIL2.

Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.