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                  學術報告
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                  A Genetic Algorithm Based Technique for Outlier Detection with Fast Convergence
                  瀏覽次數:日期:2020-01-03編輯:信科院 科研辦

                  報告人澳大利亞南昆士蘭大學  張吉教授

                  報告時間:2020年1月16上午9:30-10:30

                  報告地點軟件大樓220會議室

                  報告摘要

                  In this work, we study the problem of subspace outlier detection in high dimensional data space and propose a new genetic algorithm-based technique to identify outliers embedded in subspaces. The existing technique, mainly using genetic algorithm (GA) to carry out the subspace search, is generally slow due to its expensive fitness evaluation and long solution encoding scheme. In this talk, I will discuss a novel technique to improve the performance of the existing GA-based outlier detection method using a bit freezing approach to achieve a faster convergence. Through freezing converged bits in the solution encoding strings, this innovative approach can contribute to fast crossover and mutation operations and achieve an early stop of the GA that leads to more accurate approximation of the fitness function. This research work can contribute to the development of a more efficient search method for detecting subspace outliers. The experimental results demonstrate the improved efficiency of our technique compared with the existing method.

                  報告人簡介

                  張吉,澳大利亞南昆士蘭大學計算機科學副教授(相當于英國大學系統中的Reader和美國大學系統中的正教授),博士生導師, IEEE高級會員AG网上注册AG网上注册,ACM會員AG网上注册,澳大利亞奮進學者AG网上注册,昆士蘭學者,加拿大Killam學者AG网上注册,第3屆世界智能大會特邀專家,美國密歇根州立大學AG网上注册、新加坡南洋理工大學和日本筑波大學訪問客座教授,之江實驗室高級研究員AG网上注册。張吉教授獲2011年度南昆士蘭大學杰出研究獎和澳大利亞奮進獎,2017開始入選美國Marquis世界名人堂(3%專業人士入選比例)AG网上注册。張吉教授2008年畢業于加拿大戴爾豪斯大學計算機系,獲博士學位。張吉教授也曾在國立澳大利亞聯邦科學與工業研究組織(CSIRO),加拿大多倫多大學和新加坡國立大學從事過研究工作。張吉教授的主要研究方向為大數據分析,數據挖掘,信息隱私保護及安全AG网上注册,計算智能等AG网上注册。張吉教授已在國際主要期刊和國際會議中發表研究論文150余篇。

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