D. Sheela Jeyarani ¹, A. Pethalakshmi ²

Affiliations
1 Department Of Computer Science, Mother Teresa Women’s University, Kodaikanal - 624101, Tamil Nadu, IN
2 Department of Computer Science, M.V.M. Government Arts College (W), Dindigul - 624001, Tamil Nadu, IN

Abstract

Objectives: This research paper, based on fuzzy entropy, adapts a new method along with firefly concept, seeks to select quality features. At the same time it removes redundant and irrelevant attributes in high dimensional data. Methods/Statistical Analysis: Feature selection can be understood as a data prepossessing method in order to reduce dimensionality, eliminate irrelevant data and sharpening of accuracy. In the pattern space, fuzzy entropy is used to estimate the knowledge of pattern distribution. The study of the lightning quality of the fireflies has led to the introduction of the Firefly Algorithm for computing models. This work proposes an algorithm for selecting features by integrating fuzzy entropy and firefly algorithm. Our proposed algorithm's performances are analyzed using four different high dimensional data sets WILT, ORL, LC and LTG. Findings: The algorithm which is introduced here is further experimented with four variant data sets and the results shows that this algorithm out performs the traditional feature selection method. Also our proposed algorithm achieves maximum relevance and minimum level of redundancy. The performance metrics such as sensitivity, specificity and accuracy gives significant improvement when compared with existing FCBF algorithm. Applications/Improvements: Our optimized proposed algorithm efficiently improves the performance by eliminating redundant, noisy and insignificant features and can be applied on all high dimensional data sets.

Keywords

FCBF, Feature Selection Algorithm, Firefly Algorithm, Fuzzy Entropy, High Dimensional Data.

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S. Nirmala Devi ¹, A. Pethalakshmi ²

Affiliations
1 Department of Computer Science, Government Arts College(W), Salem – 8, TN, IN
2 Department of Computer Science, MVM Government Arts College (W), Dindigul, TN, IN

Abstract

Objectives: The complex problems are solved by sharing the resources in the grid environment. The proposed ORR approach ensures the availability of resources when it is required in the grid computing environment. The ORR approach also strives to reduce the process switching which is high in TARR (Time-Slice based Resource Reservation). Methodology: The ORR maintains slice queue in which the free time slots are entered. If the free time slices are taken as such then there increase in overhead on process switching. So, the selectslicequeue maintains the slices in best-fit approach based on the current request. In this paper, samples of ten jobs in two scenarios are considered. It is compared with the existing FCFS (First Come First Serve) basis and TARR approach. Findings: In the existing FCFS approach, there is resource reservation denial because of non-availability of resources at the time slot requested. Though the resources are reserved in time-slice basis in TARR based on the defer time, the number of process switching is high. In this case even the small chunk of time-slice is used. The increased process switching increases the overhead on process suspension and resumption. Hence the introduction of bestfit strategy in ORR approach reduces the reservation denial of FCFS and also reduces the overhead on process suspension and process resumption as in TARR. Applications: The ORR scheme can be incorporated in the existing grid environment as Globus, Nimrod G, Legion etc., New agreed virtual organizations can also make use of the ORR scheme.

Keywords

Context Switching, Grid Computing, Optimal Resource Reservation, Resource Management, Slice Queue, TLB.

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