Generate Classification Rules Research Articles

Traffic Classification in Software-Defined Networking Using Genetic Programming Tools

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.

Characteristic Attribute Organization System (CAOS): Identifying Classification Rules Based on Phylogenetically Organized Sequences.

Classification is a technique that labels subjects based on the characteristics of the data. It often includes using prior learned information from preexisting data drawn from the same distribution or data type to make informed decisions per each given subject. The method presented here, the Characteristic Attribute Organization System (CAOS), uses a character-based approach to molecular sequence classification. Using a set of aligned sequences (either nucleotide or amino acid) and a maximum parsimony tree, CAOS will generate classification rules for the sequences based on tree structure and provide more interpretable results than other classification or sequence analysis protocols. The code is accessible at https://github.com/JuliaHealth/CAOS.jl/ .

Variable Gap MAG Welding Penetration Control Using Rough Set Knowledge Acquisition

The establishment of the correspondence between the important features of the topside weld pool and the penetration state is a key link in penetration control. This article presents a scheme of variable gap welding penetration control based on rough-fuzzy modeling. By utilizing the basic concepts of variable precision rough sets and conditional information entropy, relevant algorithms for attribute reduction and rule extraction in decision information systems are addressed. Raw data for modeling are obtained through current–gap combination experiments, and then rough-set-based knowledge acquisition algorithms are used to generate classification rules for the penetration state. Based on the correspondence between the important features of the topside pool and the penetration state, a fuzzy control model is established. Welding experiments under variable gaps are conducted to examine the proposed control scheme, and the results indicate that the weld has good penetration and meets the requirements of welding specifications.

IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA C4.5 UNTUK PREDIKSI EVALUASI ANGGOTA SATUAN PENGAMANAN STUDI KASUS PT. YIMM PULOGADUNG

The inability to perform security duties is a problem that has serious consequences for both the company and the members of the security force. In today's increasingly competitive environment, where companies must search for highly competent people, a more sophisticated technical approach is needed to overcome this problem. One of the techniques used in data analysis is data mining. The researchers implemented a classification method using decision trees. Decision trees were chosen because they are one of the most used techniques in data analysis and provide rules that are easy to apply. This study uses the C4.5 algorithm to generate classification rules that decide whether a member of a security unit should be retained in a company. The results of this study showed an accuracy rate of 99.84%, demonstrating excellent ability to predict the incompetence of security unit members. Through this approach, data regarding the incompetence of security force members can be presented in a more professional and effective manner, helping companies make more informed decisions about personnel. their affairs

Developed ABCLASS-Miner Classification Algorithm Based Rule Extraction for Denim Fabrics

Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture.

Generating simple classification rules to predict local surges in COVID-19 hospitalizations

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Analysis of Application of Tennis Electronic Referee Based on Artificial Intelligence in Tennis Matches

In today’s era of rapid development of artificial intelligence, with the continuous development of technology, electronic referees have become an important part of many sports events, and artificial intelligence technology has become the brain of competitive sports and integrated into major events. this article uses artificial intelligence algorithms, graph theory and ID3 algorithms to establish an analysis of whether electronic referees violate rules in tennis matches. And use post-pruning technology to realize the pruning of the classification decision tree, generate classification rules, and complete the construction of the classification decision tree model used by the tennis electronic referee in the tennis match. The experimental results show that the application analysis of the artificial intelligence-based tennis electronic referees in tennis matches explored in this paper is more recognized and liked by athletes and spectators, and the athletes’ perception of the fairness of the game has increased by about 55%.

Preventing the generation of inconsistent sets of crisp classification rules

In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a classification model based on collections of crisp rules is to use sets (unordered collections) instead of lists (ordered collections). One of the problems associated with sets is that multiple rules may cover a single instance but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms, named CFSGS and CFSBE, do not generate classification rules nor classification models but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. We analyzed both algorithms from a theoretical perspective and conducted experiments with a proof of concept evolutionary algorithm that employs CFSBE. The experiments suggest that using CFSBE as an embedded tool does incur a computational overhead, but such cost is not prohibitive.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naive Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers.

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Selection of Anthropometric Variables and Methods for Classification of Obesity: In a case of Korean Females

Body mass index (BMI) is most widely used as an indirect measure of fat mass due to its convenience. However, according to previous studies, the poor degree of agreement in obesity classification between BMI and percent body fat was found (Al-Mohaimeed, Ahmed, Dandash, Ismail, & Saquib, 2015; Flegal et al., 2009; Freedman & Sherry, 2009); usefulness of the obesity classification referring to the predefined BMI cut-off value has been controversial. For this reason, this study aimed to find a new method for obesity classification by using four artificial intelligence (AI) techniques and two statistical methods for classification. To enhance its applicability and convenience, we tried to classify obesity with a minimum number of required input body dimensions. The methods were discriminant analysis (DA), kth-nearest-neighbor (kNN), decision tree (DT), support vector machine (SVM), neural networks (NN), and fuzzy rule-based systems (FRBS). FRBS which is an accurate and interpretable classification method can deal with uncertainty and imprecision of the reasoning process, unlike other methods. The performance of the classification methods was achieved in the aspect of accuracy and interpretability. In this study, accuracy refers to the ratio of correct predictions from all predictions made by a model and interpretability refers to the ease of interpretation and application of classification rules. We tried to consider a trade-off between accuracy and interpretability, so that the result could be practically applied to obesity classification. In Korea, females whose body fat percentage are greater than 30% diagnosed as obesity (W. K. Kim, 2010). We defined obesity according to this value. Dataset was obtained from SizeKorea (SizeKorea, 2010). The dataset was collected from 3,224 females aged between 15 and 69 and included variables such as age, height, weight, body fat percentage and BMI. After outliers or omitted cases were eliminated, we analyzed data of which size is 3,180. All possible combinations of variables, which were BMI, height, weight, were used as predicting variable set for each experiment. All analysis conducted in this paper were implemented by R. According to the result, the accuracy of all methods was ranged from 58.77% to 80.22%. Age, in addition to BMI and height/weight, did not give a dramatic effect to improve accuracy; height or weight combined by BMI showed the highest accuracy, in average. DA, DT and SVM accurately classified for all combinations of variables. Except the variable set composed with height and weight, FRBS showed a good performance in the aspect of accuracy, as well. The kNN and NN methods were less precise than the methods previously mentioned. Nonetheless, all methods performed better than the conventional method with the BMI cut-off value. In the case of result with BMI and weight, the accuracy of all methods excluding kNN was more than 10% higher than the accuracy of the method using BMI cut-off values. Among the methods, DA, DT, and FRBS generated classification rules, but in different forms. Rules from DA for classification was in a form of linear equation of two variables, and rules from DT was in a form of tree, which included root, internal nodes and branches. FRBS produced a set of rules expressed in the form of “If A and/or B, then C”, where A, B and C were fuzzy sets. An example of FRBS rule is that “if one person has BMI, which is greater than 25.08 and weight greater than 63.30kg, then she is obese”. DA and DT showed different results if the analysis were iterated. When we compared trials of FRBS, the number of rules was different, but the result became almost identical after eliminating the non-dominant rules. From this, it could be inferred that the result from FRBS was most stable and consistent. Therefore, we suggest that fuzzy rule-based system (FRBS) be the most appropriate method. FRBS performed as accurate as other AI algorithms and DA, and the method provided more stable and consistent classification rules than others. Therefore, this study suggests FRBS using BMI and height or weight for classifying obesity. However, this study has a limitation regarding the level of obese. In a future study, we can subdivide the classes of obesity and conduct the same analysis to investigate methods that are more appropriate. In addition, the methodology can be applied to the male population.

Identifying Body Size Group Clusters from Anthropometric Body Composition Indicators

Mining of anthropometric data by clustering technique would categorically classify homogenous body size group. The objective of this study is to classify homogenous human body size according to anthropometric body composition indicators. Anthropometric data was measured from 382 men and 391 women of Orissa, India. Percent body fat was calculated from the skinfold parameters. Cluster analysis was then applied with self reported age, stature, weight and percent body fat. The clusters formed were tested statistically for their validity of formation. Multivariate analysis considering age, stature, weight and percent body fat revealed significant differences among men and women (p<0.001). Expectation Maximization (EM) estimated five clusters for men and women by age, stature, weight and percent body fat. Consequently, k-means cluster algorithm was used and it formed five clusters with cumulative increment in stature, weight and percent body fat, for both men and women. However, age individually, did not influence the body size indicators. The clusters were named small, medium, large, X-large and XX-large. Silhouette plot validation of clusters reveals that for both men and women, 95.5% and 98.7% of data, respectively were well-clustered. These cluster results further can generate classification rules to categorize subsequent unseen cases, and may aid in anthropometric database creation, nutritional status, body growth research, etc.

Hybrid Image Classification using ACO with Fuzzy Logic for Textured and Non-Textured Images

Background/Objectives: Classification is the most important tasks of decision making problems. It is used to group pixels into different groups in the image processing. It is frequently used to extract the land cover information, and to give a label to the area of the interest in the image. Methods/Statistical analysis: In this paper, a Hybrid classification approach, by combing the Ant Colony Optimization (ACO) and Fuzzy logic features is proposed. This approach is used to generate classification rules from the training set of the image. A measure of similarity for each pixel is calculated, which is almost same for the same class of the pixels with help of the proposed approach. Findings: It became a challenging task to classify textured and non-textured images in the presence of the coarse pixels values. The existing classification approaches such as statistical, knowledge-based and neural networks have many limitations in this context. In the training process the classification rules are generated. These rules are then given as input to the rule pruning process to further optimize the rules set. The generated classification rules are applied on the test set. It has been observed that the findings are providing better results even in the presence of mixed pixels. Application/Improvements: In this pixels of same image are being grouped into different groups. It can be extended further to apply this approach on the same category of the images for classification and Analysis.

Slr0006-like proteins: A TsaC/TsaC2/YciO subfamily exclusive to cyanobacteria

The universally conserved TsaC/TsaC2/YciO family of proteins is essential for the N6-threonylcarbamoyladenosine modification present in almost all ANN-decoding tRNAs. Previously, the family has been grouped into the TsaC/TsaC2 and YciO subfamilies. We used sequence analysis, phylogenetic methods and homology modeling to show that a third subfamily, the Slr0006-like subfamily, exists exclusively in some cyanobacteria. The Slr0006-like proteins are solely found together with both TsaC and YciO homologs, indicating a distinct function for the Slr0006-like subfamily. Accordingly, the homology models show that the amino acids in their putative binding clefts differ significantly. Hence, we introduce a new cyanobacterial subfamily of proteins with the TsaC-domain fold, along with the generated classification rules to assign new members to the correct cyanobacterial subfamily.

Semantic recognition of workpiece using computer vision for shape feature extraction and classification based on learning databases

In this study, the Knowledge Discovery Database (KDD) was used to aid the computer to visualize, recognize and classify workpieces automatically through intelligent identification. In order to acquire initial statistical shape features, smooth and segment workpieces images were accurately taken and the shape features were stored into a database. Rough Set and Relevancy Analysis was used to reduce the shape features in order to generate the minimum characteristic vector. Finally, the generated classification rules were used to guide semantic recognition as prior knowledge. The algorithm is verified on four different types of workpieces, and the results showed that the obtained shape features are suitable for object classification and recognition semantically. This paper provides a new technique of image processing of machine vision and has great significance in the future study.

TEXT CLASSIFICATION USING MODIFIED MULTI CLASS ASSOCIATION RULE

This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.

A novel method of case representation and retrieval in CBR for e-learning

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the relationship between student characteristics and learning performance, DM to generate classification rules for learning outcomes which are further used to generate cases for the case base and CBR for reasoning. This adaptive system helps in facilitating the course content of different difficulty level to individuals according to their features. The result shows the above method provides the learning material to student as per their need and helps them to enhance their learning.

Audio Classification based on Association and Hybrid Optimization Technique

pattern recognition areas and data mining, audio data classification is a most important topic. This paper describes a new classification method, where Optimal Classification Rule Extraction for multi-class Audio Data (O-cREAD). This classification method uses a new hybrid optimization approach for extracting optimal classification rules, and then these optimal rules are further used for classifying multi-class testing audio data to their respective classes with better accuracy. The optimal classification rule extraction is a two- step process. In the first step, frequent itemsets are generated by the hybrid apriori algorithm and generates classification rules using the association concept. Next, a new hybrid optimization approach is used for optimizing classification rules of classification method. The new hybrid optimization approach is based on Ant Colony Optimization (ACO) and Multi-Objective Genetic Algorithm (MOGA). The best feature of classification method (O-cREAD) is that size of classification rules can be dramatically reduced and produce more sophisticated or complicated rules to improve classification accuracy for classifying a real audio dataset into their respective classes. KeywordsData, Classification Rules, Hybrid Apriori Algorithm (H-AA), Genetic Algorithm (GA), Ant colony optimization (ACO), Hybrid optimization approach.

Use of a Recursive-Rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset

Abstract To assist physicians in the diagnosis of breast cancer and thereby improve survival, a highly accurate computer-aided diagnostic system is necessary. Although various machine learning and data mining approaches have been devised to increase diagnostic accuracy, most current methods are inadequate. The recently developed Recursive-Rule eXtraction (Re-RX) algorithm provides a hierarchical, recursive consideration of discrete variables prior to analysis of continuous data, and can generate classification rules that have been trained on the basis of both discrete and continuous attributes. The objective of this study was to extract highly accurate, concise, and interpretable classification rules for diagnosis using the Re-RX algorithm with J48graft, a class for generating a grafted C4.5 decision tree. We used the Wisconsin Breast Cancer Dataset (WBCD). Nine research groups provided 10 kinds of highly accurate concrete classification rules for the WBCD. We compared the accuracy and characteristics of the rule set for the WBCD generated using the Re-RX algorithm with J48graft with five rule sets obtained using 10-fold cross validation (CV). We trained the WBCD using the Re-RX algorithm with J48graft and the average classification accuracies of 10 runs of 10-fold CV for the training and test datasets, the number of extracted rules, and the average number of antecedents for the WBCD. Compared with other rule extraction algorithms, the Re-RX algorithm with J48graft resulted in a lower average number of rules for diagnosing breast cancer, which is a substantial advantage. It also provided the lowest average number of antecedents per rule. These features are expected to greatly aid physicians in making accurate and concise diagnoses for patients with breast cancer.