Classification Final

8/10/2019 Classification Final

1/15

S E M I N A R O N

A SURVEY OF CLASSIFICATIONTECHNIQUES ON BIG DATA


2/15

AgendaIntroductionClassification techniques

Comparative study

Conclusion

References

15-Oct-14


3/15

IntroductionClassification is the techniques that maps the data into predefined classes and groupsPredict group membership for data instance

Knowledge discoveryFuture plan

Predicts categorical class labels

Classifies data ( constructs a model ) based on the training set and the values ( class la

classifying attribute and uses it in classifying new data

15-Oct-14


4/15

Introduction (Cont.)Classification

Supervised

Human-guidedclassification

Predictive ordirected

Decision tree

Unsupervised

Calculated bysoftware

Descriptive orundirected

K- means

15-Oct-14


5/15

Classification Techniques

SupervisedClassification

Techniques

DecisionTree

SupportVector

Machine

NaveBayes

Nearest Neighbor

15-Oct-14


6/15

Decision TreeIt is a flow chart like a tree structure which classify instances by sorting its attribute values

It generates the rule for the classification of the dataset

Algorithms

Iterative Dichotomer(ID3)

C4.5 Classification RegressionTree (CART)

Measure Entropy information gain Gini diversity indexTop-down procedure Construct Binary DT

Pruning through single pass algorithm Post pruning based on cost

15-Oct-14


7/15

Bayesian Network It is a graphical model for set of various variable features

Show high accuracy and speed

ProbabilisticLearning Incremental

ProbabilisticPrediction Standards

15-Oct-14


8/15

Nearest Neighbor The heuristic techniques are used to select the good k

It has some strong consistency results

Instance-based classifiers work by storing training records and using them to predict the clalabel of unseen cases

15-Oct-14


9/15

Support Vector Machine ( SVM )

It trains classifier to predict the class of the new sample

Key Implementation

Mathematical

programming

KernelFunction

15-Oct-14


10/15

Support Vector Machine ( SVM )(Cont.)

Algorithms

Linear Non-linear Non separable use

Data is linearly separable Not suitable for C classhypothesis

Noisy data is available

15-Oct-14


11/15

Comparative StudyTechniques Advantages Disadvantages

Decision tree Simple to understand and interpret Requires little data preparation

Locally-optimal decisions are each node

Do not generalize well from thtraining data

Bayesian Network

Able to handle noisy data Well suited for continuous features

Training time will be large Poor interoperability Require parameters

K- nearestneighbor

Easy to understand Implement classification technique

Computational costs are expen The local data is very sensitiv

require large storage

Support vectormachine

Finds the best classification functionof training data

Prevent over fitting than othermethods

Computationally expensive Require large time and storage Poor interpretability of results

15-Oct-14


12/15

Cont.

* SVM prediction speed and memory usage are good

** Nave Bayes speed and memory usage are good

*** Nearest neighbor usually has good prediction in low dimensions

Algorithms Predictive

Accuracy

Fitting

Speed

Prediction

Speed

Memory

Usage

Easy to

Interpret

Trees Low Fast Fast Low Yes

SVM High Medium * * *

Nave Bayes Low ** ** ** Yes

Nearest

Neighbor

*** Fast *** Medium High No

15-Oct-14


13/15

ConclusionHere I have discussed various classification techniques such as Decision tree, Bayesian ne

Nearest neighbor and Support vector machine

Decision tree and SVM have different operational profiles where one is accurate and otheand vice versa

15-Oct-14


14/15

References1. Seema Sharma, Jitendra Agrawal, Shikha Agarwal, Sanjeev Sharma, Machine Learning Te

Data Mining: A Survey, 979 -1-4799-1597-2/13,2013, IEEE

2. Krisztian Balog, Heri Ramampiaro , Cumulative Citation Recommendation: ClassificationACM 978-1-4503-2034-4/13/07, 2013

3. Mohd Fauzi bin Othman, Thomas Moh Shan Yau, Comparison of Different Classification TUsing WEKA for Breast Cancer,520 -523, Springer-Verlag Berlin Heidelberg 2007

4. Francesco Ricci, lior Rokach, Bracha Shapira , Paul B, Kantor, Recommender System Handb

ISBN 978-0-387-85819-7 Springer Science + Business Media, LLC 2011

15-Oct-14


15/15

15-Oct-14

Documents

Classification Final