Classification performance of various real-life data sets when the features are discretized

2002 ◽  
Author(s):  
R.S. Lynch ◽  
P.K. Willett
Keyword(s):  
Real Life ◽  
Classification Performance ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

scholarly journals A New Three-parameter Xgamma Fréchet Distribution with Different Methods of Estimation and Applications

2021 ◽  
pp. 291-308
Author(s):  
Mohamed Ibrahim Mohamed ◽  
Laba Handique ◽  
Subrata Chakraborty ◽  
Nadeem Shafique Butt ◽  
Haitham M. Yousof
Keyword(s):  
Real Life ◽  
Estimation Methods ◽  
Data Sets ◽  
Clear Preference ◽  
Life Data ◽  
Fréchet Distribution ◽  
Real Life Data ◽  
Proposed Model ◽  
Frechet Distribution

In this article an attempt is made to introduce a new extension of the Fréchet model called the Xgamma Fréchet model. Some of its properties are derived. The estimation of the parameters via different estimation methods are discussed. The performances of the proposed estimation methods are investigated through simulations as well as real life data sets. The potentiality of the proposed model is established through modelling of two real life data sets. The results have shown clear preference for the proposed model compared to several know competing ones.

scholarly journals The Gompertz Gumbel II Distribution: Properties and Applications

2020 ◽  
pp. 1-15
Author(s):  
Adebisi Ade Ogunde ◽  
Gbenga Adelekan Olalude ◽  
Donatus Osaretin Omosigho
Keyword(s):  
Real Life ◽  
Stochastic Ordering ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Data Sets ◽  
Monte Carlo Simulation Study ◽  
Life Data ◽  
Real Life Data ◽  
Decreasing Failure Rate ◽  
New Distribution

In this paper we introduced Gompertz Gumbel II (GG II) distribution which generalizes the Gumbel II distribution. The new distribution is a flexible exponential type distribution which can be used in modeling real life data with varying degree of asymmetry. Unlike the Gumbel II distribution which exhibits a monotone decreasing failure rate, the new distribution is useful for modeling unimodal (Bathtub-shaped) failure rates which sometimes characterised the real life data. Structural properties of the new distribution namely, density function, hazard function, moments, quantile function, moment generating function, orders statistics, Stochastic Ordering, Renyi entropy were obtained. For the main formulas related to our model, we present numerical studies that illustrate the practicality of computational implementation using statistical software. We also present a Monte Carlo simulation study to evaluate the performance of the maximum likelihood estimators for the GGTT model. Three life data sets were used for applications in order to illustrate the flexibility of the new model.

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

Past Extropy of 𝑘-Records

2020 ◽  
Vol 35 (1) ◽  
pp. 25-38 ◽  
Author(s):  
E. I. Abdul Sathar ◽  
Jitto Jose
Keyword(s):  
Uniform Distribution ◽  
Random Sample ◽  
Real Life ◽  
Random Variables ◽  
Data Sets ◽  
Life Data ◽  
The Past ◽  
Real Life Data

AbstractRecently, A. S. Krishnan, S. M. Sunoj and N. U. Nair [Some reliability properties of extropy for residual and past lifetime random variables, J. Korean Stat. Soc. 2020, 10.1007/s42952-019-00023-x] introduced past extropy for measuring uncertainty contained in past lifetime of random variables. In the present study, we focus on the past extropy of k-records. The motivation for considering past extropy of k-records has been discussed in detail. We have also illustrated the merit of considering past extropy of k-records over past extropy of classical records and past extropy of original random sample using two real life data sets. Some important properties of past extropy of k-records are discussed in this work. We have expressed past extropy of k-records using past extropy of k-records arising from uniform distribution. The work proposes a simple estimator for past extropy of k-records as well.

A New Selection Measure for Classification Using Decision Trees

2004 ◽  
Vol 03 (01) ◽  
pp. 1-7
Author(s):  
B. Chandra ◽  
Gaurav Saxena
Keyword(s):  
Decision Trees ◽  
Classification Accuracy ◽  
Gini Index ◽  
Real Life ◽  
Data Sets ◽  
Life Data ◽  
The Past ◽  
Real Life Data ◽  
Selection Measures ◽  
Balance Factor

The paper proposes a new selection measure for classification using decision trees for Data mining. Various algorithms have been proposed in the past for classification using decision trees viz. ID3, CART, SLIQ, etc. Selection measures like the Gain, Gain ratio, and Gini index have been proposed in these algorithms. However, none of the selection measures developed so far take into account the balancing of trees. This paper proposes a new selection measure which also takes into account the balancing of trees that will facilitate in improving the classification accuracy. The performance of the original SLIQ algorithm, C5 and the algorithm using the new selection measure (which takes into account the accuracy as well as the balance factor) was measured on the basis of the classification accuracy. Three real life data sets were chosen for this purpose.

scholarly journals Uncertainty Measures in Ordered Information System Based on Approximation Operators

2014 ◽  
Vol 2014 ◽  
pp. 1-17
Author(s):  
Bingjiao Fan ◽  
Weihua Xu ◽  
Jianhang Yu
Keyword(s):  
Information System ◽  
Real Life ◽  
Data Sets ◽  
Approximation Accuracy ◽  
Approximation Quality ◽  
Life Data ◽  
Uncertainty Measures ◽  
Real Life Data ◽  
Uncertainty Measurement ◽  
Lower And Upper Approximations

This paper focuses on constructing uncertainty measures by the pure rough set approach in ordered information system. Four types of definitions of lower and upper approximations and corresponding uncertainty measurement concepts including accuracy, roughness, approximation quality, approximation accuracy, dependency degree, and importance degree are investigated. Theoretical analysis indicates that all the four types can be used to evaluate the uncertainty in ordered information system, especially that we find that the essence of the first type and the third type is the same. To interpret and help understand the approach, experiments about real-life data sets have been conducted to test the four types of uncertainty measures. From the results obtained, it can be shown that these uncertainty measures can surely measure the uncertainty in ordered information system.

scholarly journals A Generalized Modification of the Kumaraswamy Distribution for Modeling and Analyzing Real-Life Data

2020 ◽  
Vol 8 (2) ◽  
pp. 521-548
Author(s):  
Rafid Alshkaki
Keyword(s):  
Real Life ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Data Sets ◽  
Kumaraswamy Distribution ◽  
Life Data ◽  
Real Life Data ◽  
Beta Power ◽  
Special Cases

In this paper, a generalized modification of the Kumaraswamy distribution is proposed, and its distributional and characterizing properties are studied. This distribution is closed under scaling and exponentiation, and has some well-known distributions as special cases, such as the generalized uniform, triangular, beta, power function, Minimax, and some other Kumaraswamy related distributions. Moment generating function, Lorenz and Bonferroni curves, with its moments consisting of the mean, variance, moments about the origin, harmonic, incomplete, probability weighted, L, and trimmed L moments, are derived. The maximum likelihood estimation method is used for estimating its parameters and applied to six different simulated data sets of this distribution, in order to check the performance of the estimation method through the estimated parameters mean squares errors computed from the different simulated sample sizes. Finally, four real-life data sets are used to illustrate the usefulness and the flexibility of this distribution in application to real-life data.  

scholarly journals On modified Kies distribution and its applications

2017 ◽  
Vol 51 (1) ◽  
pp. 41-60
Author(s):  
C. SATHEESH KUMAR ◽  
S. H. S. DHARMAJA
Keyword(s):  
Maximum Likelihood ◽  
Hazard Function ◽  
Real Life ◽  
Simulated Data ◽  
Maximum Likelihood Estimators ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data ◽  
Method Of Maximum Likelihood ◽  
Simulated Data Sets

In this paper, we consider a class of bathtub-shaped hazard function distribution through modifying the Kies distribution and investigate some of its important properties by deriving expressions for its percentile function, raw moments, stress-strength reliability measure etc. The parameters of the distribution are estimated by the method of maximum likelihood and discussed some of its reliability applications with the help of certain real life data sets. In addition, the asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is examined by using simulated data sets.

scholarly journals A New Kumaraswamy Generalized Family of Distributions with Properties, Applications and Bivariate Extension

2020 ◽  
Author(s):  
Muhammad H. Tahir ◽  
Muhammad Adnan Hussain ◽  
Gauss Cordeiro ◽  
Mahmoud El-Morshedy ◽  
Mohammed S. Eliwa
Keyword(s):  
Maximum Likelihood ◽  
Real Life ◽  
Unit Interval ◽  
Likelihood Method ◽  
Data Sets ◽  
Data Set ◽  
Life Data ◽  
Real Life Data ◽  
The Family ◽  
Family Of Distributions

For bounded unit interval, we propose a new Kumaraswamy generalized (G) family of distributions from a new generator which could be an alternate to the Kumaraswamy-G family proposed earlier by Cordeiro and de-Castro in 2011. This new generator can also be used to develop alternate G-classes such as beta-G, McDonald-G, Topp-Leone-G, Marshall-Olkin-G and Transmuted-G for bounded unit interval. Some mathematical properties of this new family are obtained and maximum likelihood method is used for estimating the family parameters. We investigate the properties of one special model called a new Kumaraswamy-Weibull (NKwW) distribution. Parameter estimation is dealt and maximum likelihood estimators are assessed through simulation study. Two real life data sets are analyzed to illustrate the importance and flexibility of this distribution. In fact, this model outperforms some generalized Weibull models such as the Kumaraswamy-Weibull, McDonald-Weibull, beta-Weibull, exponentiated-generalized Weibull, gamma-Weibull, odd log-logistic-Weibull, Marshall-Olkin-Weibull, transmuted-Weibull, exponentiated-Weibull and Weibull distributions when applied to these data sets. The bivariate extension of the family is proposed and the estimation of parameters is given. The usefulness of the bivariate NKwW model is illustrated empirically by means of a real-life data set.

scholarly journals USING REAL-LIFE DATA WHEN TEACHING STATISTICS: STUDENT PERCEPTIONS OF THIS STRATEGY IN AN INTRODUCTORY STATISTICS COURSE

2013 ◽  
Vol 12 (2) ◽  
pp. 59-70 ◽  
Author(s):  
DAVID L. NEUMANN ◽  
MICHELLE HOOD ◽  
MICHELLE M. NEUMANN
Keyword(s):  
Student Perceptions ◽  
Teaching Method ◽  
Statistics Education ◽  
Real Life ◽  
First Year ◽  
Data Sets ◽  
Life Data ◽  
Research Journal ◽  
Real Life Data ◽  
Statistics Course

Many teachers of statistics recommend using real-life data during class lessons. However, there has been little systematic study of what effect this teaching method has on student engagement and learning. The present study examined this question in a first-year university statistics course. Students (n= 38) were interviewed and their reflections on the use of real-life data during the classes were coded into themes. Resulting themes were (a) relevant perspective in learning, (b) interest, (c) learn/remember material, (d) motivation, (e) involvement/engagement, and (f) understanding of statistics. The results indicate both cognitive and affective/motivational factors are associated with using real-life data to teach statistics. The results also suggest the features in data sets statistics teachers should look for when designing their lessons. First published November 2013 at Statistics Education Research Journal Archives

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