Titlebook: Advances in Knowledge Discovery and Data Mining, Part II; 14th Pacific-Asia Co Mohammed J. Zaki,Jeffrey Xu Yu,Vikram Pudi Conference procee

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https://doi.org/10.1007/978-981-16-1037-0ctor machines are combined efficiently. The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray. We explain why and how the proposed method works well. The experimental results on two bench

FLING

Ethics: Informed Consent, Patient Privacy P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification. However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) loc

Volatile-Oils

Quality Control and Quality Assuranceses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a u

Matrimony

Arunraj Navaratnarajah,Michelle Willicombearacterized by high dimension and small sample size. To avoid curse of dimensionality good feature selection methods are needed. Here, we propose a two stage algorithm for finding a small subset of relevant genes responsible for classification in high dimensional microarray datasets. In first stage

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,Acute Kidney Injury in the?Elderly,retaining the same level of accuracy for a .-nearest neighbor (KNN) classifier. To achieve this goal, our proposed algorithm learns the weighted similarity function for a KNN classifier by maximizing the leave-one-out cross-validation accuracy. Unlike the classical methods PW, LPD and WDNN which can

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POWER

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https://doi.org/10.1007/1-4020-2586-6of the algorithms focus on undirected or unweighted graphs. In this work, we propose a novel model to determine the interesting subgraphs also for directed and weighted graphs. We use the method of density computation based on influence functions to identify dense regions in the graph. We present di

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