Document Classification by

Fuzzy Attribute Evaluation

ARL-TR-380 December 2000

Aivars Celmins 

Approved for public release; distribution unlimited.

^
The findings in this report are not to be construed as an

official Department of the Army position unless so

designated by other authorized documents.

Citation of manufacturer’s or trade names does not

constitute an official endorsement or approval of the use

thereof.

Destroy this report when it is no longer needed. Do not

return it to the originator.
ARL-TR-380 December 2000

Army Research Laboratory

Aberdeen Proving Ground, MD 21005-5066

Document Classification by

Fuzzy Attribute Evaluation

Aivars Celmins 

Computational and Information Sciences Directorate

Approved for public release; distribution unlimited.

^
Abstract

A technique for classifying objects is proposed that combines classification

estimates based on the properties of object attributes. The technique was

developed as an aid to classifying captured foreign documents on the battlefield. In this approach, the input information consists of linguistic assessments of the document’s classification; these assessments are based on

document attributes such as document age, format, and place of discovery.

The assessments are modeled as fuzzy sets and combined with the help of a

decision function into an output fuzzy set that represents the overall assessment of the document. For a final linguistic classification, the output of the

decision function is compared with target classes. The procedure achieves

good performance if the decision function is trained on representative sets

of classified objects.

Although the classification procedure was developed for classification of

captured documents, it might be also applied to target recognition from

approximate sensor inputs, triage procedures and diagnostics in medical

praxis, risk assessments, and similar problems where classification requires

the combination of uncertain judgments.

ii
Contents

1 Introduction 1

2 Attribute Evaluation 3

2.1 Document Attributes ....................... 3

2.2 Linguistically Scaled Significance Categories ......... 4

3 Significance Accumulation 5

3.1 Decision Function ......................... 5

3.2 Target Categories ......................... 6

3.3 Proximity Indicators ....................... 7

3.4 Decision Function Training ................... 8

3.5 Generation of Training Sets ................... 9

3.6 Linguistic Interpretation . . . . . . . . . . . . . . . . . . . . . 11

4 Example 13

4.1 Training and Test Sets . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Training Results . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5 Conclusions 16

References 17

Distribution 19

Report Documentation Page 21

Figures

1 Membership functions of linguistic significance categories . . . 4

2 Membership function changes when parameters are modified . 6

3 Effects of weight parameters on decision function ........ 7

4 Membership functions of captured document categories .... 7

5 Data set with sample correlation 0.9 . . . . . . . . . . . . . . . . 10

iii
6 Example of a decision process . . . . . . . . . . . . . . . . . . . . 12

7 Training history . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8 Classification of a training set . . . . . . . . . . . . . . . . . . . . 15

9 Classification of a test set . . . . . . . . . . . . . . . . . . . . . . . 15

Tables

1 Document attributes ......................... 3

iv
1. Introduction

Captured foreign documents can be an important source of vital information on the battlefield. The handling of such documents is regulated by

Army Field Manual 34-52 [1] (pp 4-2 to 4-4), which stipulates that captured

documents should be assigned to one of four categories (named A, B, C,

and D), depending on the contents of the documents, and that the documents should be dealt with according to their categories. Most important

are documents of category A, which require immediate action; documents

of categories B and C are less important, and documents of category D can

be discarded.

A problem with this classification can arise when the documents are not in

English, because then the contents of captured documents might not be obvious. Some help is provided by the FALCON system [2], which scans the

documents and provides a quick translation and a simple computer analysis of the translated text (mainly by keyword searching). The soldier can

use this analysis to assess the importance of the documents and eventually

to categorize the documents.

Two improvements to the system are being considered. First, it is proposed

to analyze the original text instead of its English translation [3]. Second,

the input information for classification decisions is being extended beyond

text analysis to other document attributes, such as document date, circumstances of capture, document type, etc. Under ideal circumstances, when

the documents are in English, such additional attributes need not be considered, because an understanding of the text outweighs all other information. However, additional document attributes can be important when the

contents of the documents are available only through a cursory computer

analysis. Therefore, for an automatic document classification, particularly

when the source language is not English, the input from nontextual attributes should be fused with outputs from computer text analysis. This report addresses the incorporation of nontextual document assessments into

the classification procedure.

In the approach presented here, the importance of each document is expressed by a numerical significance value that is based on the combined

significance indicators obtained from the attributes of the document. The

attributes can be either outputs from text analysis or the other document

properties mentioned above, and the significance indicator values are combined by a decision function that computes an accumulated significance

value. The significance indicators can be vague linguistic expressions (such

as “medium importance,” “low importance,” etc). These expressions, as

well as outputs from text analysis, are modeled by fuzzy sets on a significance scale from zero to unity. The decision function accumulates these

1
fuzzy sets by calculating an output fuzzy set that represents the significance of the document. Finally, the accumulated significance is compared

with standard significance categories (for instance, the Army’s A, B, C, and

D categories) and the result interpreted and formulated in linguistic form,

such as “the document is approximately category A,” “the document is

likely secret,” etc.

Section 2 describes common document attributes and corresponding significance indicators. Section 3 treats the computation and training of the

decision function, and section 4 provides examples of document classification by attributes. A summary and conclusions are given in section 5.

2
2. Attribute Evaluation

2.1 Document Attributes

Table 1 gives a tentative list of attributes that might contribute to document classification and can be assessed without an understanding of document content. (Eventually, actual documents will be used to establish a

more comprehensive list.) The assumption is that to obtain the significance

level of a document, one would inspect the document and estimate values

of significance indicators for all available attributes. Each “significance indicator” is a linguistic assessment of document significance derived from

properties of an attribute; the value of the indicator is assessed for individual attributes independently of the properties of other attributes. For each attribute, the table lists some properties that might be used in estimating the

value of the significance indicator. For example, the value of the indicator

for the attribute “age of document” might be estimated as “medium high”

if the document is current, and “low” if the document is several months

old. Note that any attribute can have an entry equivalent to “unknown,”

which is treated as “not significant.” Such an entry does not affect the accumulated document significance value.

Table 1. Document attributes.

Attribute Indicator Attribute Indicator

Age of document Current (present date) Style of document Typed

Recent (few days old) Handwritten

Weeks Printed

Months Fax

Unknown Carbon, photographic, or similar copy

Format of document Military order Circumstances of Troop quarters

Report discovery Abandoned house

Letter Office

Indistinct File cabinet

Stationery Military stationery Open field

Business stationery On a person

Loose leaf Text analysis* Frequency of military keywords

Notebook Military acronyms

Standard military expressions

Frequency of a particular keyword class

Text arrangement in military standard form

*There might be several indicators for this group, depending on the sophistication of the text analysis program.

The list is a short tentative selection of possible subgroups.

3
2.2 Linguistically Scaled Significance Categories

Values of the significance indicators are assessed in linguistic terms. For

this I use a five-category scale of fuzzy numbers from Chen and Hwang

[4] (p 468) with the categories “low,” “medium low,” “medium,” “medium

high,” and “high.” The membership functions of these numbers are shown

in figure 1. For present purposes, the five-category scale is supplemented

with two extreme categories, which correspond to “unknown significance”

and “extremely significant.” The former is represented by a crisp singleton

at s = 0 (zero significance), and the latter is a crisp singleton at s = 1. The

result is thus a scale of seven categories, a range that knowledge engineers

consider optimal for linguistic estimates [4].

Figure 1. Membership

functions of linguistic

significance categories.

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

low

medium

low medium

medium

high high extreme

unknown Membership

Significance

4
3. Significance Accumulation

3.1 Decision Function

Let a ̃i be the value of the significance indicator from attribute i. The indicators are crisp or fuzzy numbers between zero and unity. An overall

significance level for the document is obtained by the accumulation of the

a ̃i with the help of a decision function. Let n be the number of attributes of

a given document. Then the accumulated significance S ̃ of that document

is computed by

S ̃ = 1 −

i

=n

i=1

(1 − mi · a ̃i)

wi

0.5[1+exp(0.1·(1−n))]

. (1)

This decision function formula contains two sets of crisp parameters: attribute modifiers mi and attribute weights wi. These parameters make the

decision function adaptable to particular applications. Their values are determined by a training procedure performed on representative sets of classified documents (see sect. 3.4).

The modifiers mi are positive and enter the formula for S ̃ as crisp multipliers of the fuzzy indicators a ̃i. (For numerical reasons, the lower bound

of the multipliers is set equal to 0.001.) The purpose of the modifiers is to

change the fuzzy significance indicators by increasing or decreasing their

values. Because the significance measure s is restricted to the interval [0,1],

a special truncating multiplication is used (instead of an ordinary multiplication) and indicated in equation (1) by mi · a ̃i. That multiplication

truncates the product to the unit interval and assigns to the abscissa s = 1

a membership value that equals the maximum membership of those parts

of the product that have an abscissa larger than one. For instance, if the

multiplication by a large factor mi shifts the support of a ̃i completely out

of the unit interval, then the product mi · a ̃i is a crisp singleton at the

significance level s = 1 (linguistically, “extremely significant”).

The effects of the modifying parameters on significance membership functions are illustrated by figure 2. The original membership (“medium significance”) is labeled with m = 1. If the modifying parameter is less than unity,

the membership function is shifted to the left, and its spread is reduced. For

modifying parameters larger than unity, the membership function is shifted

to the right, its spread increases, and eventually (for large m) it is shifted

out of the unit interval. In that case, the product equals a singleton at s = 1.

The weights wi are restricted to values larger than or equal to 0.1 and enter the formula as exponents of the contributions from the attributes i. A

weight wi has in essence the same effect as a wi-times repetition of the ith

5
Figure 2. Membership

function changes when

parameters are

modified.

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

1.0 1.5 1.8

Significance

Membership

m = 0.5

estimate a ̃i. Figure 3 shows the effect of weight parameters on a decision

function, for simplicity assuming only one attribute and a modifying parameter m1 = 1. If the weight parameter w1 = 1, the membership function of S ̃ equals the input membership, labeled with w = 1. If the weight

parameter is less than unity, the input membership function is shifted to

the left, and its spread is reduced. Hence, its effect is similar to that of a

small modifying parameter. For this reason, to avoid redundant parameter adjustments, I set the minimum permissible value of wi to 0.1 (instead

of zero). For wi > 1, the membership of the decision function is shifted

to the right and its spread reduced. It never shifts out of the unit interval.

This is different from the effects of the modifying parameters, and it adds

flexibility to the decision function.

The exponent of the square brackets in equation (1) has been determined

experimentally. It reduces the contributions of individual significance indicators when the number n of attributes is large. The accumulated significance S ̃ of the document is a fuzzy set with support between zero and unity

on the significance scale s.

3.2 Target Categories

The purpose of document classification is to assign each document to a category from a predefined set of target categories. In the approach presented

here, the assignments are done by comparison of the accumulated significance S ̃ (a fuzzy set) with fuzzy sets that represent the target categories.

Target categories are in this case the four captured document categories defined in the relevant Army Field Manual [1]. These categories are modeled

as fuzzy sets over the significance scale shown in figure 4. For consistency

with the set of input categories, the set is supplemented with two extreme

categories, D− and A+, which are represented as singletons at s = 0 and

s = 1, respectively. This is an arbitrary representation of the Army’s categories, because the latter are defined in terms of the textual contents of

the documents and not by their significance level. However, because the

analysis presented here is based on fuzzy-set representation and the decision function is determined by a training procedure, a fuzzy-set representation of the target categories is adequate. It suffices that the membership

functions of the four categories are in ascending order; the details of the

functions are less important.

6
Figure 3. Effects of

weight parameters on

decision function.

Significance

Membership

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

w = 0.5 1.0 2.0 3.0

Figure 4. Membership

functions of captured

document categories.

Significance

Membership

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

D– C B A A+ D

3.3 Proximity Indicators

To train the decision function, one needs a measure of the goodness of document classification, that is, a measure of the deviation of a category implied by the decision function from the known category of a document.

Because the accumulated significance and the target categories are fuzzy

sets, the goodness measure must be a measure of disparities between fuzzy

sets. For present purposes, the disparity between an accumulated significance S ̃ and a target category C ̃ is expressed by three proximity indicators,

which are referred to as separation, discord, and exclusion.

The separation is the difference between the defuzzified values of S ̃ and

C ̃, where the defuzzified values are computed by the center of gravity

method. Let μS(s) and μC(s) be the membership functions of S ̃ and C ̃,

respectively, and GS and GC be the corresponding defuzzified values. The

defuzzified value GS (the center of gravity of μS(s)) is computed by

GS =

 1

0 s μS(s) ds

 1

0 μS(s) ds

(2)

and GC is computed correspondingly. The separation of S ̃ from a target

category C ̃ is defined by

PSC = GS − GC. (3)

The separation PSC can have any value between –1 and 1, and its sign indicates whether S ̃ is mainly to the left or mainly to the right of C ̃.

The discord [5] between the two fuzzy sets S ̃ and C ̃ is defined by

DSC = 1 − maxs



min μS(s), μC(s)

	

. (4)

7
The discord varies between zero and unity. It equals zero when the cores of

the two fuzzy numbers intersect, and it equals unity when their supports

do not intersect. The discord expresses the lack of intensity of coincidence

between the two fuzzy sets.

The exclusion is a measure for the lack of overlap of the membership functions C ̃ and S ̃. The exclusion EC(S) of a target category C ̃ from S ̃ is defined

by

EC(S) = 1 −

 1

0 min 


μS(s), μC(s)



ds

 1

0 μC(s) ds . (5)

Note that the exclusion is not symmetric. It equals zero when the membership function μC(s) of the target category C ̃ is contained entirely in the set

μS(s). It equals unity when S ̃ and C ̃ do not intersect. Otherwise EC(S) is

positive and less than or equal to unity. If μC(s) is a singleton at s = sC,

then the exclusion is defined by

EC(S) = 1 − μS(sC). (6)

(In this case, the exclusion and the discord are identical.)

For the training of the decision function, a disparity between two fuzzy sets

S ̃ and C ̃ is defined by

dS(C) =



D2

SC + E2

C(S)

2

1/2

· PSC. (7)

The disparity dS(C) is a crisp number between –1 and +1.

3.4 Decision Function Training

The relative importance of attributes for document classification is modeled

by the values of the decision function parameters mi and wi. To determine

the proper values of these parameters, one would classify a set of documents with known significance levels and find such parameter values that

the document set overall is correctly classified. Let the training set consist

of k documents with significance categories C ̃j , j = 1, ... ,k. Each document is also characterized by a set of significance indicators according to

its attributes. The significance indicators are accumulated by the decision

function (eq (1)), providing for each document a fuzzy document significance S ̃j . The goal of the training is to modify the decision function parameters such that the fuzzy sets S ̃j are close to the corresponding target sets

C ̃j . As a measure of agreement, I use the sum U of squares of disparities

between the accumulated significances and the target categories, where the

disparities are computed with equation (7):

U(m1, m2, ... ,mn, w1, w2, ... ,wn) = 1

k



j=k

j=1




d

(j)

S(C)

2

. (8)

8
In equation (8), d

(j)

S(C) is the disparity between the accumulated significance

S ̃j and the target significance C ̃j for the document j. The training parameters are the attribute modifiers mi and the weights wi; see equation (1).

They are determined by a steepest descent algorithm on the objective function U in the parameter space. The partial derivatives of U that are needed

for the steepest descent algorithm are numerically approximated by difference quotients.

3.5 Generation of Training Sets

In generating synthetic “documents” for use in training sets, one must

model the attributes that characterize a given document set. The most useful attributes in this context are those that correlate highly with the classification of the document. For instance, assume that for a certain document

type, two attributes (e.g., date and keywords) classify the document correctly most of the time. In this instance, the significance indicators from

these two attributes should correlate with the document significance level.

Therefore, training sets that represent that document type should be so constructed that sample correlation coefficients between the significance indicators of the two attributes and the corresponding document categories

have values close to unity.

This section outlines the construction of training sets with prescribed correlations that can be used to test the training program. In real-life applications, a training set consists of documents that are considered typical for

the relevant scenario.

Let the number of documents in the training set be k. Each “document” in

the training set is represented by its target class Cj , j = 1, ... ,k and a list

of significance indicators a ̃

(j)

i :

Cj , a ̃

(j)

1 , a ̃

(j)

2 , a ̃

(j)

3 , ... , a ̃(j)

n .

For construction of the training sets, sample correlation coefficients ρi, i =

1, ... ,n are prescribed between a ̃

(j)

i and Cj .

The construction of training sets begins with a set of k crisp data points

(xj , yj ), j = 1, ... ,k. The sample correlation coefficient of the k data pairs

(xj , yj ) is defined by

γk(x, y) =





k

j=1

(xj − x ̄)

2

k

j=1

(yj − y ̄)

2





−1/2



k

j=1

(xj − x ̄)(yj − y ̄), (9)

where x ̄ and y ̄ are average values of the xj and yj , respectively. To obtain

a set with a prescribed positive correlation γo, one can proceed as follows.

First, two different values x1 and x2 are chosen at random, and corresponding y-values are set equal to the x-values: y1 = x1 and y2 = x2. Next, the

remaining xj for j = 3, ... ,k are chosen at random, and the corresponding

9
yj are calculated such that the correlation

γj (x, y) = 

1 − (1 − |γo|) · (j/k)

	

· sgn(γo). (10)

The calculation is done by numerical search alternating above and below

the line through the first two data points. If the prescribed γo is negative,

then one can use the same algorithm by changing the initial y-values to

y1 = x2 and y2 = x1.

For this algorithm to be used for document generation, some adjustments

are necessary. First, the values of x and y are restricted to the unit interval,

and corresponding restrictions for the random choice and search algorithm

apply. Second, for the initial data, one can use without loss of generality

x1 = 0.2 and x2 = 0.8 instead of random values. Then only the subsequent

xj are chosen randomly from a uniform distribution over the unit interval.

Finally, for generating randomized yj , the algorithm for the computation

of yj was augmented as follows. Let yˆj be a value of y that produces the

desired correlation coefficient γj , and let

Yj = y1 + (y2 − y1)

xj − x1

x2 − x1

(11)

be the ordinate of the intersection point of the line x = xj with the line

through the first two data points. The value yj is randomly chosen from an

interval bounded by

ybase = Yj +(ˆyj − Yj ) · (j/k) (12)

and

yend = min

1, max 
0, ybase + 2(ˆyj − ybase)



. (13)

The interval shrinks as j increases, and it has zero length for the final data

point with j = k, which ensures a correct final correlation coefficient for

the set. Figure 5 shows a set with 100 data points, one attribute, and a

sample correlation coefficient of 0.9, which was generated by the described

algorithm.

Figure 5. Data set with

sample correlation 0.9.

Document significance

Attribute significance

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

10
A conversion of the crisp data into fuzzy categories is needed to generate fuzzy significance estimates. For conversion (fuzzification) of the data,

the y-axis was subdivided in segments corresponding to the linguistic categories (fig. 1) and the x-axis in segments corresponding to the Army’s

categories (fig. 4); fuzzy values a ̃

(j)

i and Cj were assigned according to the

compartment in which each point (xj , yj ) was located.

3.6 Linguistic Interpretation

The output of the decision function is a fuzzy set S ̃ on the significance

scale. One obtains a linguistic interpretation of the result by comparing S ̃

with fuzzy sets that represent target categories. The comparison is done

in terms of the disparity dS(C) between fuzzy sets, and the document is

assigned to the category that is closest to the accumulated significance in

terms of the disparity. If S ̃ is between two target categories, then a corresponding hedge is added to the linguistic interpretation (for example,

“with low confidence”).

As an illustration of the classification procedure, consider a simple case

with only three attributes of equal weights (wi ≡ 1) and modifying parameter values m1 = 1.5, m2 = 0.4, and m3 = 0.1. Let the corresponding significance indicators from the three attributes be “low medium,” “medium,”

and “high.” Figure 6(a) shows the input fuzzy sets. After multiplication

with the parameters mi, one obtains a modified set of three indicators,

shown in figure 6(b). Accumulation of these three indicators by equation

(1) produces a fuzzy set S ̃, shown in figure 6(b) by a heavy solid line. Next,

S ̃ is compared with target categories (see fig. 4); the comparison is shown

in figure 6(c). Obviously, S ̃ is between categories B and C, and somewhat

closer to B. The linguistically formulated result in this example is as follows:

Document category is approximately B: Likely SECRET information with low

confidence.

11
Figure 6. Example of a

decision process:

(a) input sets (line styles

represent different sets);

(b) sets after

multiplication (solid

line is fuzzy set S ̃); and

(c) comparison of S ̃

(solid line) with target

categories (dashed

lines).

Significance

Membership

0 0.2 0.4 0.6 0.8 1 0

0.2

0.4

0.6

0.8

1 m = 1.5 m = 0.4 m = 0.1

C B

Significance

Membership

0 0.2 0.4 0.6 0.8 1 0

0.2

0.4

0.6

0.8

1

Significance

Membership

0 0.2 0.4 0.6 0.8 1 0

0.2

0.4

0.6

0.8

1

(a)

(b)

(c)

12
4. Example

4.1 Training and Test Sets

Consider an example of training that was performed on a training set of 100

synthetic “documents,” generated as described in section 3.5. The number

of attributes is n = 4, so that the input from each document consists of five

items:

Ctarget, a ̃1, a ̃2, a ̃3, a ̃4.

The prescribed sample correlation coefficients ρi = ρ(Ctarget, a ̃i), i = 1, 2, 3, 4,

were as follows:

ρ1 = 0.9, ρ2 = 0.9, ρ3 = 0.1, ρ4 = −0.9.

Accordingly, the first two attributes of the documents provide mostly correct information about document significance, while the information from

attributes 3 and 4 is mostly false. We would therefore expect that, after the

training, the first two attributes will be weighted more heavily than the last

two.

A test set, also consisting of 100 “documents,” was established in the same

manner, with the only difference being that a different seed number was

used in the random number generation routine.

4.2 Training Results

The training of the decision function required 36 iterations. The training

history is illustrated in figure 7. Figure 7(a) displays the value of the objective function U over the number of iteration steps. It shows that a minimum of U was found after about 29 iterations; the iteration was continued

because of conservative iteration end conditions.

Figure 7(b) shows the development of the modifying parameters mi. By

the end of the iteration, the parameters of the “good” attributes, m1 and

m2, had settled to values of about unity. The “bad” attributes (3 and 4)

both converged to very small values, making the contributions from these

attributes negligible. The value m3 = 0.001 is the lower bound for the modifying parameters.

Figure 7(c) shows the development of the weight parameters wi. The

weights of the “good” parameters converge to values close to 0.4. The

weight of w3 of attribute 3 has a similar value, but that attribute is already

eliminated by its very small modifying parameter. The weight of attribute

4 converges to the smallest permissible value, 0.1. Because the parameters

mi and wi have, for small values, similar effects on the decision function, an

attribute can be eliminated by a small value of either parameter, and there

13
Figure 7. Training

history: (a) value of U

over iteration;

(b) development of

parameters mi; and

(c) development of

parameters wi.

Iteration step

Objective function

5 10 15 20 25 30 35 40 0

0.04

0.08

0.12

0.16

0.2

Iteration step

Multipliers 

m

5 10 15 20 25 30 35 40

0

0.2

0.4

0.6

0.8

1

1.2

1.4

ρ = 0.9

ρ = 0.9

ρ = 0.1

ρ = –0.9

Iteration step

Weights 

w

5 10 15 20 25 30 35 40 0

0.2

0.4

0.6

0.8

1

= min

(a)

(b)

(c)

is no improvement from reducing the corresponding parameter from the

other parameter set.

The final values of the parameters were as follows:

m1 = 1.251 m2 = 0.813 m3 = 0.001 m4 = 0.173

w1 = 0.382 w2 = 0.360 w3 = 0.326 w4 = 0.100

In principle, attribute 4 with its large negative correlation could be used for

classification if I inverted its significance indicator. However, the present

setup of the decision function training does not allow such a usage: the

model is based on the assumption that significance information from attributes is correct. If this is not the case for documents in the training set,

then the corresponding attribute is suppressed.∗

The capability of the trained decision function to classify documents of the

training set is illustrated in figure 8. This figure shows the distributions of

∗A modification of the decision function that allows it to handle negative correlations is

presented elsewhere [6].

14
Figure 8. Classification

of a training set.

Attribute number

Documents, (%)

01234

0

20

40

60

80

classification errors by attribute significance indicators and, with the label

“0,” the classification errors by the decision function. For this display, a classification error is expressed in terms of the difference between the correct

document class and the implied class by attributes and decision function,

respectively. Because there are six target categories, a classification error

is an integer with values between −5 and +5. Accordingly, the error distributions are shown in 11 bins for each prediction. The figure shows that

attributes 1 and 2 classify about 40 percent of the documents correctly and

the remaining documents mostly with an error of +1 or −1 category. The

classification errors of attribute 3 (with correlation 0.1) are randomly distributed, and the classification errors of attribute 4 (with correlation −0.9)

have a bimodal distribution. The distribution labeled “0” shows the distribution of classification errors by the trained decision function. It correctly

classifies over 60 percent of the documents, which indicates that combining

attribute significance indicators does indeed improve classification quality.

To test the success of the training, I prepared a test set of 100 documents

with the same characteristics as the training set (i.e., with the same correlations between attribute significance indicators and document classes). The

classification results for this set are shown in figure 9 in the same form as

figure 8. The distributions of classification errors have a pattern similar to

that in figure 8 and confirm the success of the training.

Figure 9. Classification

of a test set.

Attribute number

Documents, %

01234

0

20

40

60

80

15
5. Conclusions

A classification procedure has been devised in which attribute-provided

classifications are combined with the help of a fuzzy decision function. The

decision function contains parameters that are determined by the function

being trained on representative training sets. The trained function classifies with fewer errors than classifications based on any single attribute. Because of the simplicity of the decision function and the transparency of

the roles of the parameters, the training also provides indications about

the importance of each attribute. Unimportant attributes are indicated by

small parameter values and can be disregarded in subsequent classifications, thereby simplifying the classification process.

To improve the performance of the described classification method, an investigation of the effects of the decision function’s structure would be helpful. In particular, the function parameters mi and wi might be supplemented

or replaced by different parameters and the effects of such changes on classification performance studied.

The described classification procedure has potential applications well beyond the immediate use described in this report. Possible applications include document classification, target recognition, triage procedures, diagnostics, risk assessment, and other classification problems where the result

depends on attribute properties.

16
References

1. Intelligence Interrogation, Field Manual 34-52, Headquarters of the Department of the Army, Washington, DC (28 September 1992).

2. F. Fisher and C. R. Voss, “FALCON, An MT System Support Tool

for Non-Linguists,” Proc. Advanced Information Analysis Conference,

McLean, VA (1997).

3. V. V. Cross and C. R. Voss, “Fuzzy Ontologies for Multilingual Document Exploitation,” Proc. 18th International Conference of the North American Fuzzy Information Processing Society (NAFIPS-99), pp 392–397, IEEE

Publications, Piscataway, NJ (1999).

4. S.-J. Chen and C.-L. Hwang, Fuzzy Multiple Attribute Decision Making,

Springer-Verlag, Berlin (1992).

5. A. Celmin ̧ s,ˇ “Least Squares Model Fitting to Fuzzy Vector Data,” Fuzzy

Sets Syst. 22, 245–269 (1987).

6. A. Celmin ̧ s,ˇ “Classification with a Fuzzy Decision Function,” Proc. 8th

International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), pp 267–270, Universidad

Politecnica de Madrid, Spain (2000).  ́

17

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298-102

Document Classification by Fuzzy Attribute

Evaluation

December 2000 Apr 99 to Jan 00

A technique for classifying objects is proposed that combines classification estimates based on the

properties of object attributes. The technique was developed as an aid to classifying captured

foreign documents on the battlefield. In this approach, the input information consists of linguistic

assessments of the document's classification; these assessments are based on document attributes

such as document age, format, and place of discovery. The assessments are modeled as fuzzy sets

and combined with the help of a decision function into an output fuzzy set that represents the

overall assessment of the document. For a final linguistic classification, the output of the decision

function is compared with target classes. The procedure achieves good performance if the

decision function is trained on representative sets of classified objects.

Although the classification procedure was developed for classification of captured documents, it

might be also applied to target recognition from approximate sensor inputs, triage procedures

and diagnostics in medical praxis, risk assessments, and similar problems where classification

requires the combination of uncertain judgments.

Fuzzy decision function, significance accumulation, fuzzy data fusion

Unclassified

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ARL PR: 0TE320

AMS code: 611102H411

DA PR: AH44

PE: 611102H44

Approved for public release;

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Aivars Celmins

email:

Aberdeen Proving Ground, MD 21005-5066

^