Special topics on text mining[Part I: text classification]

Hugo Jair Escalante, Aurelio Lopez, Manuel Montes and Luis Villaseñor

Multi label text classification

Hugo Jair Escalante, Aurelio Lopez, Manuel Montes and Luis Villaseñor

Most of this material was taken from: G. Tsoumakas, I. Katakis and I. Vlahavas. Mining multi-label data. Data Mining and Knowledge Discovery Handbook, Part 6, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, pp. 667-685, 2010.

Machine learning approach to TC

• Develop automated methods able to classify documents with a certain degree of success

Training documents(Labeled)

Learning machine(an algorithm)

Trained machine

Unseen (test, query) document

Labeled document

What is a learning algorithm?

• A function:

• Given:

: df C {1,..., }C K

1,...,{( , )}i i ND y x

;di iy C x

Binary vs multiclass classification

• Binary classification: each document can belong to one of two classes.

• Multiclass classification: each document can belong to one of K classes.

: { 1,1}df

: {1,..., }df K

Classification algorithms

• (Some) classification algorithms for TC :– Naïve Bayes – K-Nearest Neighbors– Centroid-based classification– Decision trees– Support Vector Machines– Linear classifiers (including SVMs)– Boosting, bagging and ensembles in general– Random forest– Neural networks

Some of this methods were designed for binary classification problems

Linear models• Classification of DNA micro-arrays

?

x1

x2

No Cancer

Cancer

( )f b x w x1 2,x x x

0b w x

0b w x

0b w x

?

Main approaches to multiclass classification

• Single machine: Learning algorithms able to deal with multiple classes (e.g., KNN, Naïve Bayes)

• Combining the outputs of several binary classifiers:– One-vs-all: one classifier per-class– All-vs-all: one classifier per pair of classes

Multilabel classification

• To what category belong these documents:

Multilabel classification

• A function:

• Given:

: df Z {1,..., }Z L K

1,...,{( , )}i i ND Z x

;di iZ L x

Conventions

X={xij}

n

mxi

y ={yj}

wSlide taken from I. Guyon. Feature and Model Selection. Machine Learning Summer School, Ile de Re, France, 2008.

Conventions

X={xij}

n

mxi

Z ={Zj}

w

|L|

Slide taken from I. Guyon. Feature and Model Selection. Machine Learning Summer School, Ile de Re, France, 2008.

Multi-label classification

• Each instance can be associated to a set of labels instead of a single one

• Specialized multilabel classification algorithms must be developed

• How to deal with the multilabel classification problem?

(Text categorization is perhaps the dominant multilabel application)

Multilabel classifiers

• Transformation methods: Transform the multilabel classification task into several single-label problems

• Adaptation approaches: Modify learning algorithms to support multilabel classification problems

Transformation methods

• Copy transformation. Transforms the multilabel instances into several single-label ones

Original ML problem Transformed ML problem (unweighted)

Transformed ML problem (weighted)

Transformation methods

• Select transformation. Replaces the multilabel of each instance by a single one

Original ML problem Transformed ML problem

Max Min Rand

Ignore approach

Transformation methods

• Label power set. Considers each unique set of labels in the ML problem as a single class

Original ML problem Transformed ML problem

Pruning can be applied

Transformation methods

• Binary relevance. Learns a different classifier per each different label. Each classifier i is trained using the whole data set by considering examples of class i as positive and examples of other classes (j≠i) as negative

• How labels are assigned to new instances?

Original ML problem Data sets generated by BR

Transformation methods

• Ranking by pairwise comparison. Learns a different classifier per each pair of different labels.

Original ML problem

Data sets generated by BR

Algorithm adaptation techniques

• Many variants, including – Decision trees – Boosting ensembles – Probabilistic generative models – KNN– Support vector machines

Algorithm adaptation techniques

• MLkNN. For each test instance:– Retrieve the top-k nearest neighbors to each

instance – Compute the frequency of occurrence of each

label – Assign a probability to each label and select the

labels for the test instance

Feature selection in multilabel classification

• An (almost) unstudied topic = opportunities • Wrappers can be applied directly (define an objective

function to optimize based on a multilabel classifier)

ValidationOriginal feature set

Generation EvaluationSubset of feature

Stopping criterion

yesnoSelected subset of feature

process

From M. Dash and H. Liu. http://www.comp.nus.edu.sg/~wongszec/group10.ppt

Feature selection in multilabel classification

• An almost un-studied topic = opportunities

• Existing filter methods transform the multilabel problem and apply standard filters for feature selection

Statistics

• Label cardinality

• Label density1

1( ) | |

m

ii

LC D Lm

1

| |1( )

mi

i

LLC D

m q

Evaluation of multilabel learning

• (New) conventions:

;di iY L x 1,...,{( , )}i i ND Y x

{ : 1,..., }jL j q

Data set

Labels

iZ L Predictions of a ML classifier for

instances in D

Evaluation of multilabel learning

• Hamming loss:

• Classification accuracy:

1

| |1

| |

Ni i

i

Y ZHL

N L

1

1( )

N

i ii

ACC I Z YN

( ) 1; ( ) 0;I true I false

Evaluation of multilabel learning

• Precision:

• Recall:

1

| |1

| |

Ni i

i i

Y ZP

N Y

1

| |1

| |

Ni i

i i i

Y ZR

N Y Z

Evaluation of multilabel learning

• F1-measure

11

2 | |1

| | | |

Ni i

i i i

Y ZF

N Z Y

Suggested readings• G. Tsoumakas, I. Katakis,I. Vlahavas. Mining multi-label data. Data Mining and Knowledge Discovery

Handbook, Part 6, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, pp. 667-685, 2010.

• G. Tsoumakas, I. Katakis. Multi-label classification: an overview. International Journal of Data Warehousing, 3(3), 1—13, 2007.

• M. Zhang, Z. Zhou. ML-kNN, A lazy learning approach to multi-label learning. Pattern recognition 40:2038—2048, 2007.

• M. Boutell, J. Luo, X. Shen. C. Brown. Learning multi-label scene classification. Pattern recognition 37:1757—1771, 2004.