Informatics Publishing Limited

L. Mary Gladence ¹, M. Karthi ², T. Ravi ³

Affiliations
1 Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai - 600119, Tamil Nadu, IN
2 Karapagam College of Engineering, Myleripalayam Village, Othakkal Mandapam Post, Coimbatore - 641032, Tamil Nadu, IN
3 Madanapalle Institute of Technology and Science, P.B. No:14, Kadiri Road, Angallu Village, Chittoor District, Madanapalle - 517325, Andhra Pradesh, IN

Abstract

Objectives: Ordinal regression is one which is used in Multiclass classification where there is an essential ordering among the classes. The training dataset is initially classified depending on the Random threshold values θ. Based on these values, the distance between the different class labels are predicted by one against one technique. Method: All Pairs Distance Calculation using one against one technique [APDC_1 AG 1] is Proposed to validate the work. But in the referred previous work, distance is calculated using adjacent classes, but here all pairs distance calculation is used to find the class label distance to all class label pairs. Findings: On the whole, New trained data are in the form of one dimensional representation. Here, with the knowledge of proposed work, testing data is tested with New trained data set and the results are produced. The Proposed method is seen to be ambitious when compared with previous work. Beside this, an additional set of experiments is done to study the potential quantifiability and illustratability of the proposed method when using APDC as base methodology. Improvements: Proposed work is analyzed with Kernel discriminant analysis, Logistic Regression, Classification via Regression, Multiclass Classifier and found APDC has attained better results according to all measures.

Keywords

All Pairs Distance Calculations, Hyper Line, Latent Space Representation, Multi-Class Ordinal Regression, One Against One Method, Ordinal Classification, Projection

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V. Maria Anu ¹, R. Aroul Canessane ¹, L. Mary Gladence ¹

Affiliations
1 Faculty of Computing, Sathyabama University, Chennai - 600119, Tamil Nadu, IN

Abstract

Background: RFID technology is being adopted by a number of applications including supply chain management. Due to the adoption of RFID technology challenging problems have appeared. The RFID database system handles huge amounts of path oriented, time dependent data. Methods: This paper focuses on minimizing the query processing time by using Taguchi optimization method. Simulations show how the factors considered impact RFID data processing efficiency. Findings: So the effectiveness of the system depends on the factors such as the number of tags accessed at a time, the data preprocessing techniques adopted, and selection of suitable indexing techniques and so on. However, few of the techniques adopted by existing methods focus on the efficiency of RFID data processing. Applications/Improvements: Taguchi method is applied to predict the best design combinations that achieve minimum query processing time.

Keywords

Data Compression, Data Indexing, RFID, Taguchi Method.

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