14 research outputs found
Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods
The job of software effort estimation is a critical one in the early stages
of the software development life cycle when the details of requirements are
usually not clearly identified. Various optimization techniques help in
improving the accuracy of effort estimation. The Support Vector Regression
(SVR) is one of several different soft-computing techniques that help in
getting optimal estimated values. The idea of SVR is based upon the computation
of a linear regression function in a high dimensional feature space where the
input data are mapped via a nonlinear function. Further, the SVR kernel methods
can be applied in transforming the input data and then based on these
transformations, an optimal boundary between the possible outputs can be
obtained. The main objective of the research work carried out in this paper is
to estimate the software effort using use case point approach. The use case
point approach relies on the use case diagram to estimate the size and effort
of software projects. Then, an attempt has been made to optimize the results
obtained from use case point analysis using various SVR kernel methods to
achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information
Processing (IJIP
Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor
AbstractThe major drawback of microarray data is the ‘curse of dimensionality problem’, this hinders the useful information of dataset and leads to computational instability. Therefore, selecting relevant genes is an imperative in microarray data analysis. Most of the existing schemes employ a two-phase processes: feature selection/extraction followed by classification. In this paper, a statistical test, ANOVA based on MapReduce is proposed to select the relevant features. After feature selection, MapReduce based K-Nearest Neighbor (K-NN) classifier is also proposed to classify the microarray data. These algorithms are successfully implemented on Hadoop framework and comparative analysis is done using various datasets
Large Scale Community Detection Using a Small World Model
In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones
Comparative study of real time machine learning models for stock prediction through streaming data
Taikomosios informatikos katedraVytauto Didžiojo universiteta