Different Number Of Attributes Research Articles

A Robust Predictive Model for Early Detection of Heart Disease using Machine Learning

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. This research project aims to develop a robust predictive model for the early detection of heart diseases by leveraging machine learning techniques, with a specific focus on the application of Support Vector Machines (SVM). The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using different number of attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also, by creating a reliable method to predict heart disease. Thus, we found that supervised machine learning algorithms can be used to make heart disease predictions with very high accuracy and excellent potential utility. Our proposed research study has significant scientific contributions to the medical community. Key Words: Machine learning, heart failure, cross validations, feature engineering, Naive Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest, Heart Disease.

Novel Meta-Heuristic Algorithm for Feature Selection, Unconstrained Functions and Engineering Problems

This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum, confirm that the SCMWOA algorithm performs better.

БИНАРНАЯ КЛАССИФИКАЦИЯ МНОГОАТРИБУТНЫХ РАЗМЕЧЕННЫХ АНОМАЛЬНЫХ СОБЫТИЙ КОМПЬЮТЕРНЫХ СИСТЕМ С ПОМОЩЬЮ АЛГОРИТМА SVDD

Introduction: At present, the volume of system logs of computer systems integrated into a distributed network infrastructure makes it impossible to manually check them in real time. Typically, the structure of each log record contains the numeric value of the observed attribute and a corresponding flag to mark the record as normal or abnormal. The support vector data description algorithm demonstrates high classification accuracy even with small volumes of the training sample. A feature of the algorithm is the work with a multi-attribute dataset, where each observation contains a common classifying marking. Consequently, the problem arises of reducing the set of markings of the attributes of the initial data to one marking of the entire observation. Purpose: to investigate the accuracy of the binary classification of experimental data of the Support Vector Data Description algorithm with a small volume of the training sample, provided that the data are labeled for each attribute separately. Methods: a method is proposed for solving the problem of reducing the set of markings of the attributes of the initial data to one single marking of the entire observation by means of two approaches: "normal observation" and voting by the majority principle. Two types of data are considered: ordered in time and uniformly mixed. The classification accuracy was assessed by calculating the area under the ROC curves with cross-validation for a different number of attributes. Results: a comparative analysis of observation labeling methods showed the advantage of the "completely normal observation" approach over the "majority vote" approach without "weighting". It is shown that the classification accuracy on mixed data is 7% higher compared to the variant of data ordering in time. The accuracy of the algorithm was investigated for a different number of attributes using the "completely normal observation" approach. The maximum achieved classification accuracy was about 96% when working with 6 attributes, with uniform mixing of the input dataset. A further increase in the number of attributes leads to a decrease in the average classification accuracy due to an increase in the proportion of anomalous observations. It is shown that when using uniform mixing of input data, the gain in accuracy can be increased by 15–20%. Practical relevance: the algorithm demonstrates an exponential growth in the consumption of computing resources with an increase in the amount of input data. Discussion: to achieve the maximum classification accuracy with acceptable resource consumption, it is necessary to form a compact set of input data, which most fully reflects the functioning of the computer system in normal mode.

Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making

The dual generalized Bonferroni mean operator is a further extension of the generalized Bonferroni mean operator which can take the interrelationship of different numbers of attributes into account by changing the embedded parameter. The 2-dimensional uncertain linguistic variable (2DULV) adds a second dimensional uncertain linguistic variable (ULV) to express the reliability of the assessment information in first dimensional information, which is more rational and accurate than the ULV. In this paper, for combining the advantages of them, we propose the dual generalized weighted Bonferroni mean operator for 2DULVs (2DULDGWBM) and the dual generalized weighted Bonferroni geometric mean operator for 2DULVs (2DULDGWBGM). In addition, we explore several particular cases and some rational characters of them. Further, a new approach is introduced to handle multi-attribute decision making problems in the environment of 2DULVs by the proposed operators. Finally, we utilize several illustrative examples to testify the validity and superiority of this new method by comparing with several other methods.

Impact of missing attributes on the novelty metric of Shah et al.

The a posteriori novelty metric proposed in 2003 by Shah and colleagues is still one of the most widely used in design research, constituting the reference for many experiments on creativity and effectiveness of ideation. However, recent research highlights that in the presence of ideas implementing different numbers of attributes, some non-negligible problems arise. The objective of this paper is to estimate the potential errors that can be committed when applying the metric of Shah et al. in these situations. The investigation has been performed by relying on a high number of randomly generated virtual sets of ideas. The results obtained revealed that the problem of “missing attributes”, if not opportunely faced, leads the considered metric to misleading novelty scores.

Termination Rules for Variable-Length CD-CAT From the Information Theory Perspective.

Cognitive diagnostic computerized adaptive testing (CD-CAT) aims to take full advantage of both cognitive diagnosis (CD) and CAT. Cognitive diagnostic models (CDMs) attempt to classify students into several attribute profiles so as to evaluate their strengths and weaknesses while the CAT system selects items from the item pool to realize that goal as efficiently as possible. Most of the current research focuses on developing the item selection strategies and uses a fixed-length termination rule in CAT. Nevertheless, a variable-length termination rule is more appropriate than the fixed-length rule in order to bring out the full potential of CD-CAT. The current study discussed the inherent issue of instability over different numbers of attributes with the previous termination rules (the Tatsuoka rule and the two-criterion rule), proposed three termination rules from the information theory perspective, and revealed the connection between the previous methods and one of the information-based termination rules that will be discussed, further demonstrating the instability issue. Two simulation studies were implemented to evaluate the performance of these methods. Simulation results indicated that the SHE rule demonstrated strong stability across different numbers of attributes and different CDMs and should be recommended for application.

Multiple‐attribute group decision‐making method of linguisticq‐rung orthopair fuzzy power Muirhead mean operators based on entropy weight

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are a qualitative form of q-rung orthopair fuzzy numbers (q-ROFNs) where the membership and nonmembership degrees are represented by linguistic variables. The Lq-ROFNs can describe a broader range of linguistic assessment information flexibly by adjusting the parameter q based on different situations, so they are more superior to the linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers in real application. Based on the Lq-ROFNs, we introduce the entropy measure which can be used to determine the indefiniteness of the assessment information. Then, based on the linguistic entropy measure, we further propose a method to obtain the attribute weights when the weight information is incomplete known. For aggregating the assessment information, the power average (PA) operator can reduce the influence of extreme data caused by the biased decision-makers by considering the support degree of different evaluation individuals, and the Muirhead mean (MM) operator can take the interrelationship of different numbers of attributes into account by adjusting the parameter vector based on the real situations. In this paper, based on these two operators, we firstly propose the linguistic q-rung orthopair fuzzy PA operator and linguistic q-rung orthopair fuzzy weighted PA operator. Further, for combing the advantages of the MM operator and PA operator, we propose the linguistic q-rung orthopair fuzzy power MM (PMM) operator and linguistic q-rung orthopair fuzzy weighted PMM operator, and then investigate some properties of them. Finally, a new multiple-attribute group decision-making (MAGDM) method is proposed to process the Lq-ROFNs, and some practical examples are given to illustrate the effectiveness and superiority of this new method in comparison with other existing MAGDM methods.

Simulation error in maximum likelihood estimation of discrete choice models

Maximum simulated likelihood is the preferred estimator of most researchers who deal with discrete choice. It allows estimation of models such as mixed multinomial logit (MXL), generalized multinomial logit, or hybrid choice models, which have now become the state-of-practice in the microeconometric analysis of discrete choice data. All these models require simulation-based solving of multidimensional integrals, which can lead to several numerical problems. In this study, we focus on one of these problems – utilizing from 100 to 1,000,000 draws, we investigate the extent of the simulation bias resulting from using several different types of draws: (1) pseudo random numbers, (2) modified Latin hypercube sampling, (3) randomized scrambled Halton sequence, and (4) randomized scrambled Sobol sequence. Each estimation is repeated up to 1 000 times. The simulations use several artificial datasets based on an MXL data generating process with different numbers of individuals (400, 800, 1 200), different numbers of choice tasks per respondent (4, 8, 12), different number of attributes (5, 10), and different experimental designs (D-optimal, D-efficient for the MNL and D-efficient for the MXL model). Our large-scale simulation study allows for comparisons and drawing conclusions with respect to (1) how efficient different types of quasi Monte Carlo simulation methods are and (2) how many draws one should use to make sure the results are of “satisfying” quality – under different experimental conditions. Our study is the first to date to offer such a comprehensive comparison. Overall, we find that the number of the best-performing Sobol draws required for the desired precision exceeds 2 000 in some of the 5-attribute settings, and 20,000 in the case of some 10-attribute settings considered.

Predicting heart ailment in patients with varying number of features using data mining techniques

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

Spatial data analysis using association rule mining in distributed environments: a privacy prospect

In this paper, we investigate the privacy issue that is remained in the spatial association rule mining (SARM). The main aim of SARM is to calculate the relationship among attributes according to their geographic locations. However, the major problem of the distributed data mining is the privacy and security issues, which require executing global results without disclosing private information by third parties. The problem of privacy preservation for SARM in distributed environments is controlled by the proposed algorithm, which is able to extract association among different numbers of attributes in geo-graphical distributed database with high privacy. The proposed algorithm is validated in term of data utility rate, efficiency and privacy preservation against the existing algorithms. It has been revealed that this algorithm decreases the execution time, memory requirements, and privacy failure rate when the size of database increases within the geographically distributed database environment.

Joint Maximum Likelihood Estimation for Diagnostic Classification Models.

Joint maximum likelihood estimation (JMLE) is developed for diagnostic classification models (DCMs). JMLE has been barely used in Psychometrics because JMLE parameter estimators typically lack statistical consistency. The JMLE procedure presented here resolves the consistency issue by incorporating an external, statistically consistent estimator of examinees' proficiency class membership into the joint likelihood function, which subsequently allows for the construction of item parameter estimators that also have the consistency property. Consistency of the JMLE parameter estimators is established within the framework of general DCMs: The JMLE parameter estimators are derived for the Loglinear Cognitive Diagnosis Model (LCDM). Two consistency theorems are proven for the LCDM. Using the framework of general DCMs makes the results and proofs also applicable to DCMs that can be expressed as submodels of the LCDM. Simulation studies are reported for evaluating the performance of JMLE when used with tests of varying length and different numbers of attributes. As a practical application, JMLE is also used with "real world" educational data collected with a language proficiency test.

Multi-attribute search framework for optimizing extended belief rule-based systems

The advantages and applications of rule-based systems have caused them to be widely recognized as one of the most popular systems in human decision-making, due to their accuracy and efficiency. To improve the performance of rule-based systems, there are several issues proposed to be focused. First, it is unnecessary to take the entire rule base into consideration during each decision-making process. Second, there is no need to visit the entire rule base to search for the key rules. Last, the key rules for each decision-making process should be different. This paper focuses on an advanced extended belief rule base (EBRB) system and proposes a multi-attribute search framework (MaSF) to reconstruct the relationship between rules in the EBRB to form the MaSF-based EBRB. MaSFs can be divided into k-dimensional tree (KDT)-based MaSFs and Burkhard–Keller (BKT)-based MaSFs. The former is targeted at decision-making problems with small-scale attribute datasets, while the latter is for those with large-scale attribute datasets. Based on the MaSF-based EBRB, the k-neighbor search and the best activated rule set algorithms are further proposed to find both the unique and the desired rules for each decision-making process without visiting the entire EBRB, especially when handling classification problems with large attribute datasets. Two sets of experiments based on benchmark datasets with different numbers of attributes are performed to analyze the difference between KDT-based MaSFs and BKT-based MaSFs, and to demonstrate how to use MaSFs to improve the accuracy and efficiency of EBRB systems. MaSFs and their corresponding algorithms are also regarded as a general optimization framework that can be used with other rule-based systems.

Effectiveness of Support Vector Machines in Medical Data mining

The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. One of the positive aspects is to discover the important patterns. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. In this case, data mining prepares the ability of research and discovery that may not have been evident. This paper analyzes the effectiveness of SVM, the most popular classification techniques in classifying medical datasets. This paper analyses the performance of the Naïve Bayes classifier, RBF network and SVM Classifier. The performance of predictive model is analysed with different medical datasets in predicting diseases is recorded and compared. The datasets were of binary class and each dataset had different number of attributes. The datasets include heart datasets, cancer and diabetes datasets. It is observed that SVM classifier produces better percentage of accuracy in classification. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust and effective classifier for medical data sets.

Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

Abstract Feature selection is a technique to choose a subset of variables from the multidimensional data which can improve the classification accuracy in diversity datasets. In addition, the best feature subset selection method can reduce the cost of feature measurement. This work focuses on the use of wrapper feature selection. This study use methods of sequential forward selection (SFS), sequential backward selection (SBS) and optimize selection (evolutionary) based on ensemble algorithms namely Bagging and AdaBoost by subset evaluations which are performed using two classifiers; Decision Tree and Naive Bayes. Thirteen datasets containing different numbers of attributes and dimensions are obtained from the UCI Machine Learning Repository. This study shows that the search technique using SFS based on the bagging algorithm using Decision Tree obtained better results in average accuracy (89.60%) than other methods. The benefits of the feature subset selection are an increased accuracy rate and a reduced run-time when searching multimedia data consisting of a large number of multidimensional datasets.

Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets

Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of computational costs without losing quality in the results when the proposed techniques are applied.

Analyzing rough set based attribute reductions by extension rule

An improved discernibility function for rough set based attribute reduction is defined to keep discernibility ability and remove redundant attributes without the precondition of the Positive Region. On the basis of discernibility function, the solution of rough set based attribute reduction can be found by satisfiability methods. With extension rule theory, a satisfiability method, the distribution of solutions with different number of attributes is obtained without enumerating all attribute reductions. Then, it is easy to search the attribute reduction with the smallest number of attributes. In addition, the cost of space and time is analyzed to find factors playing role in the computation of the method.

How Attribute Quantity Influences Option Choice

Products can be described by different numbers of attributes, but can the mere number of attributes presented across a choice set influence what type of options people choose? This article demonstrates that attribute numerosity tends to benefit certain types of options more than others and consequently has systematic effects on choice. Because attributes often serve as a heuristic cue for product usefulness, they benefit options that people perceive as relatively inferior on this dimension. Consistent with this perspective, five studies demonstrate that attribute numerosity benefits hedonic more than utilitarian options by increasing the extent to which the former appear useful. Consequently, increasing attribute quantity equally across the choice set shifts choice toward hedonic options, regardless of whether the attributes are hedonic, utilitarian, or mixed in nature. Consistent with this conceptualization, these effects become amplified when decision makers engage in heuristic processing and when priming makes usefulness salient. The findings have important implications for how marketers present attribute information, for public policy and consumer welfare, and for understanding argument numerosity effects in persuasion more broadly.

Active learning strategies for the deduplication of electronic patient data using classification trees

IntroductionSupervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Material and MethodsBased on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002) [6]. ResultsOn the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. ConclusionsIn record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings.

Quantization of Rough Set Based Attribute Reduction

We demonstrate rough set based attribute reduction is a sub-problem of propositional satisfiability problem. Since satisfiability problem is classical and sophisticated, it is a smart idea to find solutions of attribute reduction by methods of satisfiability. By extension rule, a method of satisfiability, the distribution of solutions with different numbers of attributes is obtained without finding all attribute reduction. The relation between attribute reduction and missing is also analyzed from computational cost and amount of solutions.

How Attribute Quantity Influences Option Choice

Products can be described by different numbers of attributes. But can the mere number of attributes presented across a choice-set influence what type of option people choose? This paper demonstrates that attribute numerosity tends to benefit certain types of options more than others and, consequently, has systematic effects on choice. Because attributes often serve as a heuristic cue for product usefulness, they benefit options that are perceived as relatively inferior on this dimension (e.g., hedonics). Consistent with this perspective, five studies demonstrate that attribute numerosity benefits hedonic more than utilitarian options by increasing the extent to which the former are perceived as useful. Consequently, increasing attribute quantity equally across the choice-set shifts choice toward hedonic options, regardless of whether the attributes are hedonic, utilitarian, or mixed in nature. Consistent with this conceptualization, these effects are amplified under heuristic processing and when usefulness is made salient through priming. The findings have important implications for how marketers present attribute information, for public policy and consumer welfare, and for understanding argument numerosity effects in persuasion more broadly.