A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Saranya, D.
- A Novel Cross Over and Mutation with Concept Hierarchy on Classification Algorithms
Authors
1 Coimbatore Institute of Information Technology, TamilNadu, IN
2 Bannari Amman Institute of Technology, TamilNadu, IN
Source
Software Engineering, Vol 6, No 4 (2014), Pagination:Abstract
Machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances, which is used to solve classification problems in many applications. this work perform the function by using OneR, Feature Selection, Attribute Oriented Induction (AOI). Concept hierarchies can be used to reduce the data collecting and replacing low-level concepts by higher level concepts. A new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using cross over and mutation with concept hierarchy. Experimental result shows proposed method has high accuracy with less execution time using UCI repository datasets.
Keywords
Attribute Oriented Induction, Feature Selection, Concept Hierarchy, Multi Level Mining, Support Vector Machine.
- A Fast Classification Algorithm Using Concept Hierarchy Algorithm
Authors
1 Coimbatore Institute of Information Technology, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 8 (2013), Pagination:Abstract
Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. The algorithm used for classification is OneR, Naive Bayes and C4.5 algorithm. This work use OneR, it is a simple classification algorithm that generates a one-level decision tree. OneR is able to infer typically simple, yet accurate, classification rules from a set of instances. This paper present Attribute Oriented Induction (AOI) has concept hierarchy as an advantage where concept hierarchy as a background knowledge which can be provided by knowledge engineers or domain experts. The experimental result shows that the proposed method of OneR with Attribute Oriented Induction program provides an accurate result by using UCI repository datasets.Keywords
One Rule, Attribute Oriented Induction, Machine Learning Algorithm, Naive Bayes Algorithm.- Ultrasonic Studies on Kidney Stone Phantom
Authors
1 School of Bio Sciences and Technology, VIT University, Vellore-632014, IN
2 School of Advanced Sciences, VIT University, Vellore-632014, IN
3 Department of Physics, SCSVMV University, Kanchipuram-631561, IN
Source
Journal of Pure and Applied Ultrasonics, Vol 35, No 2-3 (2013), Pagination: 49-51Abstract
Kidney stones are solid mass of tiny crystals found in the inner lining of the kidneys. Due to the presence of calcium, magnesium ammonium phosphates, urates, cystine and proteins the natural kidney stones vary in their compositions. Due to this difference in the composition, phantoms were prepared using a material called "BegoStone" which has the composition of alpha calcium sulphate hemihydrates. Depending on the ratio of BegoStone and water, five types of phantoms such as oxalate, cystine, Magnesium ammonium phosphate hexahydrate/Carbonate apatite, calcium hydrogen phosphate dihydrate and uric acid were prepared. To determine the ultrasonic velocity parameters, "Digital Ultrasonic Pulse Echo Velocity Meter" was used. The properties like transverse & longitudinal wave velocity measurements and density measurements were studied. Based on these measurements some of the other properties like bulk, shear, Young's modules and Poisson's ratio of the stone phantom were also calculated.Keywords
Kidney Stones, Phantom, Begostone, Ultrasonic Velocity Meter.Full Text
