deep learning vs multidimensional classification in human-guided text mining

Deep Learning

2015/07/04

Marat Zhanikeev [email protected] GI研＠天神イムズ

PDF: http://bit.do/150704

in Human-Guided

Text Mining vs

Multidimensional Classification

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Deep Learning vs MD Classifiers

• Deep Learning 08 10

◦ Feature-based: image → features → NN◦ Raw/Pixels : image → raw pixels → NN

• Multi-Dimentional Classification 04 05

◦ assigning classes to items in multiple dimensions• Human-Guided Text Mining 02

◦ Folksonomy + BigData◦ learning from empty state with gradually diminishing human feedback

08 A.Nguyen+2 "Deep Neural Networks are Easily Fooled..." IEEE CVPR (2015)

10 G.Goos+2 "Neural Networks: Tricks of the Trade" Springer LNCS vol.7700, 2nd edition (2012)

04 X.Zhu+1 "Introduction to Semi-Supervised Learning" Morgan and Claypool Publishers (2009)

05 D.Koller+1 "Probabilistic Graphical Models: Principles and Techniques" MIT Press (2009)

02 myself+0 "Multidimensional Classification Automation with Human Interface based on Metromaps" 4th AAI (2015)

M.Zhanikeev -- [email protected] -- Deep Learning vs Multidimensional Classification in Human-Guided Text Mining -- http://bit.do/150704 2/26...

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Deep Learning


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Deep Learning (1) Feature-Based• many feature extraction libraries, normally specific to environments/targets

• problem 1: wide range of errors, can be from 50% up to 96%

• problem 2: who decides on the features?


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Deep Learning (2) Raw Pixels• just feed the raw pixels to the Neural Network and let it sort it out for itself


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Deep Learning (3) Google Faces• a feature-based method, extremely specific, recently acquired by Google


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Deep Learning (4) Google Cats


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Deep Learning (5) Raw/Pixel Method• a standard process for a pixel-based learning 12

• CSV files are traditional, one image becomes one line

0 1 1 … 0

1 …

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Handwriting

Black -n-white

Pixel map

Matrix in a CSV file 3

Deep Learning

3

Training Testing

12 "MNIST Dataset of Handwritten Digits" http://yann.lecun.com/exdb/mnist/ (2015)


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Multi-Dimensional Classification


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MDC : Binary Relevance (BR) Classes

• single dimension• not practical today, when most things exists in multi-dimensional space 06

Training Tuples x1 x2 Y1 Y2 Y3

1 0.7 0.4 1 1 0 2 0.6 0.2 1 1 0 3 0.1 0.9 0 0 1 4 0.3 0.1 0 0 0

h1: X → Y1 h2: X → Y2 h3: X → Y3

06 J.Ortigosa-Hernandez+3 "A Semi-supervised Approach to Multi-dimensional Classification..." 6th TAMIDA (2010)


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MDC : PairWise (PW) Sets

• define classes as pairs of base BR classes 06

• lower complexity, higher error rate


1 0.7 0.4 1 1 0 2 0.6 0.2 1 1 0

0.1 0.9 0 0 1 0.3 0.1 0 0 0

h1: X → Z1 h2: X → Z2

Z1 Z2 1 0 0 1 0 0 0 0



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MDC : Label Combination (LC) Method

• a class for all combinations of base BR 06

• very high complexity, still high error rate


1 0.7 0.4 1 1 0 2 0.6 0.2 1 1 0 3 0.1 0.9 0 0 1 4 0.3 0.1 0 0 0

h: X → Z

Z 1 0 0 0



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MDC : The CC Method• CC: Classifier Chains method 07 -- literally, a chain of BR classes

• controlled complexity, much better error rate, but the main problem is which order?


1 0.7 0.4 1 1 0 2 0.6 0.2 1 1 0 3 0.1 0.9 0 0 1 4 0.3 0.1 0 0 0

h1: X → Y1 h2: Y1 → Y2 h3: Y2 → Y3

h2 h1 h3

07 J.Read+3 "Classifier chains for multi-label classification" Machine Learning, Springer (2011)


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The MetroMap Classifier (MMC)


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The Metromap Concept• like a map of a train network 01

• main advantage: e2e paths in (ontology) graphs

01 myself+0 "On Context Management Using Metro Maps" 7th SOCA (2014)


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MMC : A Practical Setting

Human judgment

Auto judgement

Folksonomy


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MMC : Processing Logic• processing based on human-defined metromap, the function is similar tochaining BR classes, but with higher performance

Metromap Classifier

Human

Check Metromap

Fuzzy?

Cold? Hot?

Robot (Automatic Classification)

Bad

Input

No Yes

No

No


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MDC (MMC) vs DL(pixels)


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DL : graphics vs Text

• graphics◦ pixels are already numeric◦ images can be resized to provide same-size input -- DL needs fixed-size input

• text requires complex processing1. tokenize text (words)2. frequency distribution -- variable size

3. sample distribution -- finally, the same/fixed size!


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Experimental Setup (1) Humans• 2 main cases: hot + cold = picked but not used, hot - cold = picked and used(blackswans) 03

03 myself+0 "Black Swan Disaster Scenarios" IEICE PRMU研 (2014)


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Experimental Setup (2) Process• the text is not numeric by nature, has to be converted into sampledfrequency distribution

• calculations in R, used h2o package 11 for deep learning

0 1 1 … 0

1 …

0 …

… …

1 …

Text

Matrix in a CSV file

Deep Learning

Tokenize Frequency Distribution Sample

Bayes Many (Chains, Metromap , etc.)

Path 1

Path 2

11 "H2O: R Package for Learning Algorithms" http://cran.r-project.org/web/packages/h2o (2015)


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Results (1) MMC vs BR

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title

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title:keywords:abstract

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02 myself+0 "Multidimensional Classification Automation with Human Interface based on Metromaps" 4th AAI (2015)


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Results (2) DL Results

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Diagonal/humanDeep learningkeys(title)rule(cold#yes hot#yes)


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keys(title:keywords:abstract)rule(cold#yes hot#yes)


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keys(title:keywords:abstract)rule(cold#no hot#yes)


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• compared to x = ycase

• DL performs verybadly

• best performswhen abstract isused, even then about25% hits

• same performance forhot + cold and hot- cold cases


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That’s all, thank you ...


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MDC and Social Robotics Go Together


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Social Robotics in Text Mining Context

Rebot

(careless) Input

Human Human

{structure}

(pinpoint) Select

Browse (or use otherwise)

Some Knowledge

(folksonomies, knowledge bases, databases, indexes, ontologies, etc.)

(metromaps )


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deep learning vs multidimensional classification in human-guided text mining

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