Outlines

Intelligent Database Systems Lab

Presenter : MIN-CONG WU

Authors : LAM HONG LEE, DINO ISA, WOU ONN CHOO ,

WEN YEEN CHUE

2012.ESA

High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation• Bayesian classification as compared to other

classification approaches is its ability and simplicity in handling raw text data directly

• As a trade-off to its simplicity, Bayesian classification has been reported as one of the poorest-performing classification approaches.


Objectives

• By use to HRKE facility enhance the accuracy of Bayesian classifier without sacrificing the low cost.


Methodology – Block diagram


Methodology – Bayesian Classifier


Methodology – TF-IDF method• TF-IDF =• TF(Term Frequency)， IDF(Inverse Document Frequency)• N=This word contains the number of document in dataset• Example:

TF*IDF

Boat Aircrafts Cars Trains

coupe 5 5 90 5

engine 90 90 100 100

wheel 5 90 90 90


Methodology – HRKE facility

• The degree of relevance of keywords in the classification task can be adjusted by setting a threshold, m/n.

threshold1.0 without the

inclusion of HRKE facility.

.

.0.1


Experiment-the basic flat ranking multivariate


Experiment - Featured Articles dataset


Experiment - Vehicles dataset


Experiment - Mathematics dataset


Experiment - 20-Newsgroups dataset


Experiment - Summary


Conclusions

• HRKE facility is achieved through applying unique feature selection method based on the occurrence of keywords in documents from a specified category, and compares the occurrence of those keywords in each of the competing categories.


Comments• Advantages

Improve Bayesian classification performance and can maintain low cost.

• Applications- Feature selection

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Outlines