| Type of Material: | Thesis |
| Title: | Studies on Design of Ensembles for Efficient Learning of Diabetes Dataset |
| Researcher: | LAVANYA, T |
| Guide: | KUMARAVEL, A |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2017 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2017; D10CS010 |
| Fulltext: | Shodhganga |
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| 008 | __ | 240919t2017||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454885 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aLAVANYA, T|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aStudies on Design of Ensembles for Efficient Learning of Diabetes Dataset |
| 260 | __ | |aChennai|bBharath University, Chennai|c2017 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2017|oD10CS010 |
| 520 | __ | |aEven though there are many factors influencing the final results of the predicting exercises, we should be very careful enough to select the list of most important ones especially in the context like diagnosing diabetes diseases. We focus on dataset size, cost sensitiveness, regional influence, and prioritization of the features. Mining the data sets of different sizes or different regions many times need not yield similar results with expected maximum accuracy. Hence the data size or inherent regional characteristics act as important parameters for mining exercises. In this research studies firstly we consider data sets from two different geographical regions and the calculation of performance measures separately. Also, we get the same for integrated data set obtained by the union of the original sets independently as inverse results establishing the hypothesis for integrated data set. Secondly we consider the issue of mechanizing the prediction of new patients heart disease diagnosis based on data mining |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aKUMARAVEL, A|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/324563|yShodhganga |
| 905 | __ | |afromsg |
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