By Rajendra Prasath, Anil Kumar Vuppala, T. Kathirvalavakumar
This booklet constitutes the refereed complaints of the 3rd overseas convention on Mining Intelligence and data Exploration, MIKE 2015, held in Hyderabad, India, in December 2015.
The forty eight complete papers and eight brief papers offered including four doctoral consortium papers have been rigorously reviewed and chosen from 185 submissions. The papers disguise quite a lot of themes together with details retrieval, desktop studying, development acceptance, wisdom discovery, category, clustering, picture processing, community safeguard, speech processing, traditional language processing, language, cognition and computation, fuzzy units, and company intelligence.
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Extra resources for Mining Intelligence and Knowledge Exploration: Third International Conference, MIKE 2015, Hyderabad, India, December 9-11, 2015, Proceedings
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De/data/datasets/rgbd-dataset/download 15. : A benchmark for the evaluation of RGB-D slam systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 573–580. fr Dhofar University, Salalah, Sultanate of Oman Abstract. Feature subset selection is a key problem in the data-mining classification task that helps to obtain more compact and understandable models without degrading their performance. This paper deals with the problem of supervised wrapper based feature subset selection in data sets with a very large number of attributes and a low sample size.
Fig. 3. Flowchart of the transformation process. 3 Experimental Investigations The experiments were conducted on three biological datasets – Central Nervous System (CNS), a large data set concerned with the prediction of central nervous system embryonal tumor outcome based on gene expression. This data set includes 60 samples containing 39 medulloblastoma survivors and 21 treatment failures. These samples are described by 7129 genes [4]. – the Leukemia microarry gene expression dataset that consists of 72 samples which are all acute leukemia patients, either acute lymphoblastic leukemia (47 ALL) or acute myelogenous leukemia (25 AML).
The results are presented in Table 8 and Fig. 1. It can be seen that in most of the cases, the class based feature selection is helpful in improving the classification. In data sets where the number of classes Table 4. No. features Cl1 Cl2 Cl3 Cl4 Cl5 Cl6 Cl7 Cl8 Cl9 Cl10 64 32 42 41 37 35 37 36 43 34 37 Table 5. No. S. Devi Table 6. No. of features chosen in each class for letter recog. features Cl 1 16 Cl 2 Cl 3 Cl 4 Cl 5 Cl 6 Cl 7 Cl 8 Cl 9 Cl 10 11 10 10 10 9 14 11 11 11 11 Cl 11 Cl 12 Cl 13 Cl 14 Cl 15 Cl 16 Cl 17 Cl 18 Cl 19 Cl 20 10 11 10 10 12 12 14 9 11 11 Cl 21 Cl 22 Cl 23 Cl 24 Cl 25 Cl 26 11 15 9 13 12 10 Table 7.