A process for predicting manhole events in Manhattan

被引：37

作者：

Rudin, Cynthia ^{[1
]}

Passonneau, Rebecca J. ^{[2
]}

Radeva, Axinia ^{[2
]}

Dutta, Haimonti ^{[2
]}

Ierome, Steve ^{[3
]}

Isaac, Delfina ^{[3
]}

机构：

[1] MIT, Alfred P Sloan Sch Management, Cambridge, MA 02139 USA

[2] Columbia Univ, Ctr Computat Learning Syst, New York, NY 10115 USA

[3] Consolidated Edison Co New York Inc, New York, NY 10003 USA

来源：

MACHINE LEARNING | 2010年 / 80卷 / 01期

关键词：

Manhole events; Applications of machine learning; Ranking; Knowledge discovery; KNOWLEDGE DISCOVERY; PRIORITIZATION;

D O I：

10.1007/s10994-009-5166-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a knowledge discovery and data mining process developed as part of the Columbia/Con Edison project on manhole event prediction. This process can assist with real-world prioritization problems that involve raw data in the form of noisy documents requiring significant amounts of pre-processing. The documents are linked to a set of instances to be ranked according to prediction criteria. In the case of manhole event prediction, which is a new application for machine learning, the goal is to rank the electrical grid structures in Manhattan (manholes and service boxes) according to their vulnerability to serious manhole events such as fires, explosions and smoking manholes. Our ranking results are currently being used to help prioritize repair work on the Manhattan electrical grid.

引用

页码：1 / 31

页数：31

共 45 条

[1]

[Anonymous], SUBLANGUAGE STUDIES

[2]

[Anonymous], 2003, Journal of machine learning research

[3]

[Anonymous], 2002, P 40 ANN M ASS COMP

[4]

[Anonymous], 1980, Content analysis

[5]

Azevedo A., 2008, Proceedings of Informatics and Data Mining, P182

[6]

Becker H, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P86

[7]

Boriah S., 2008, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, P857, DOI DOI 10.1145/1401890

[8] The use of the area under the roc curve in the evaluation of machine learning algorithms [J].

Bradley, AP .

PATTERN RECOGNITION, 1997, 30 (07) :1145-1159

[9]

CASTANO R, 2003, WORKSH MACH LEARN TE

[10] Prioritization of areas in China for the conservation of endangered birds using modelled geographic distributions [J].

Chen, GJ ;

Peterson, T .

BIRD CONSERVATION INTERNATIONAL, 2002, 12 (03) :197-209

← 1 2 3 4 5 →