Knowledge Engineering Techniques for Evaluation of Steel Market ...

Knowledge Engineering Techniques for Evaluation of Steel Market ...

Artificial intelligence, expert systems, marketing researchAdam STAWOWY *Bogdan RĘBIASZ *Andrzej MACIOŁ *Knowledge Engineering Techniques for Evaluation of Steel Market in PolandMarket research focuses on the entire marketplace for the organisation's (countries) products. One of marketresearch purpose is to assess the overall size of the market for specific products. The authors met this kind of problem inevaluation of steel market in Poland. At the moment Poland is undergoing the process of steel sector restructuring. Theforecast of the domestic consumption of steel products is one of the most significant as well as most controversialaspects of this restructurization.In this paper we compare three different techniques employed as tools for classification of examples. There are:neural network, evolutionary algorithm and ID3 algorithm. The database depicts some attributes of national economic ofdifferent countries in different phases of their development and the steel consumption in these countries. The decisiontree inducted by mentioned techniques has to recognise a forthcoming steel consumption in Poland (or other developingcountries) on the base of anticipated growth factors.1. INTRODUCTIONMarket research focuses on the entire marketplace for the organisation's (countries) products.One of market research purpose is to assess the overall size of the market for the specific products.This is usually done on both a short- and a long-term basis. Such “market definition” can bedifficult. Potential market size can rapidly change as economic conditions and technology change.The authors met this kind of problem in evaluation of steel market in Poland [12].One of the most sophisticated method to solve this problems is using an expert systems whichrecognise historic patterns in steel demand and both the magnitude and the structure of economicfactors.Anyone desiring to create a knowledge base for such a system must deal with the fundamentalproblem of knowledge acquisition. The most popular technology, giving the set of rules (or decisiontrees) to properly classify objects from predefined set, is learning from examples. The problemconsists in searching for relationships and global patterns that exist in databases, but are “hidden”among vast amounts of data.In this paper we compare three different techniques employed as tools for classification ofexamples. There are: neural network, evolutionary algorithm and ID3 algorithm. The databasedepicts some attributes of national economic of different countries in different phases of theirdevelopment and the steel consumption in these countries. The decision tree inducted by mentioned*Faculty of Management, University of Mining and Metallurgy, Krakow

techniques has to recognise a forthcoming steel consumption in Poland (or other developingcountries) on the base of anticipated growth factors.The key information needed for a potential market analysis are the following:1. information about the economy and the trends characteristic of it; probable influence ofeconomic trends on demand for particular products,2. information about past sales and sales trends in the industry as a whole (or in the sector which isbeing analysed),3. information about competing substitution products.Regardless what the information source is, any marketing information system must be capableof effectively processing the data. The problem encounters here is the fact that even when there is alarge set of data it is often not possible to discover easily all the relationships and interdependenciesbetween parts of it. Traditionally, this task has been done by experts. On the other hand, analysescarried out by a person, or even by a group of people, are by definition subjective and may beinaccurate.2. MARKET RESEARCH IN THE STEEL INDUSTRYThe problem of devising effective marketing information systems is especially important inthose sectors of the economy which produce investments goods and where direct methods of marketresearch are not sufficient. The steel and iron industry is one of such sectors.Investment cycles in steel industry are very long. Because of very large capital costs itrequires, excessive development of production capabilities in this sector is extremely costly andexerts enormous influence on the economics of metallurgic production. In the second part of 70-tiesand at the beginning of the 80-ties, the highly-developed countries experienced a rapid growth of theproduction capabilities of steel industry [4]. At the beginning of the 80-ties, for instance, the realproduction capabilities of British steelworks were 2-3 times greater than the actual production. Thiswas the result of the difficulty to establish the factors which determine steel products consumptionas well as of not carrying out careful analyses of the future steel consumption. Such a situationnecessitated a significant reduction of production capabilities and the number of employees inBritish steel sector in the 80-ties. In the years 1973-1985 the number of L-D converters in thatcountry had been reduced from 25 to 14 while the number of employees had decreased by 412 000[1].At the moment Poland is undergoing the process of steel sector restructuring. The forecast ofthe domestic consumption of steel products is one of the most significant as well as mostcontroversial aspects of this restructurization.Forecasting of the future steel product demand is usually done by means of one of thefollowing methods: the correlation method/analysis which assumes that there is a quantitative relationship betweenthe increase of the steel products consumption and the increase of the GDP, the direct method which takes into account the predicted dynamics of the development of thosesectors of the economy which are the main consumers of steel products, the analogy method which presumes that the volume and structure of steel productsconsumption tends to change is such a way as to become similar to that of better-developedcountries. The ratio which is the most often used here is the steel products consumption percapita.In the simplest case, the steel products consumption in the correlation method can berepresented by the following function:Steel products consumption = steel absorption of GDPGDP

This forecast assumes a relationship between steel consumption and total GDP. The accuracyof the forecast in this method depends on establishing the correct value for the steel absorption forthe period of the forecast. The steel absorption can be defined as the amount of steel productsconsumption per one unit of GDP; often this ratio seems to be established in an arbitrary way,without performing any deeper studies.In the direct method, we predict demand by end-use industry and product type according tothe following mathematical relationship:where:Dtni1D0i Gti SD t - demand in year t,i=1..n - denotes all i end-use industries (sectors),D - consumption in the base year in i end-use industry,0itGi - the sectoral growth expectation by the year t,tSi- represents a consumption intensity factor for the product and sector, describing productsubstitution, changes in product mix, reflecting sectoral quality changes etc.The key importance in preparing forecasts using this method is an understanding of:current demand by product type and end-use industry,likely sectoral growth dynamics, including changes in total sectoral steel consumption per unitof output,sectoral changes in product mix.In the analogy method the accuracy of the forecast depends on the following factors: selection of a country or a group of countries in relation to which the forecast is being prepared, establishing the speed of achieving the parameters values characteristic of the country which isthe point of reference in a given situation.As it was in the case of the correlation method these parameters also seem to be chosenarbitrarily by experts preparing the forecast.3. KNOWLEDGE ACQUISITION: SYMBOLIC LEARNING, NEURAL NETWORK,EVOLUTIONARY ALGORITHMIn artificial intelligence, various techniques have been developed and tested in the past twodecades for the purpose of reducing complexity and/or uncertainty. Among these techniques,systems based on human or expert heuristics and algorithms which exhibit learning capability havegained significant attention of researchers.3.1. SYMBOLIC LEARNINGSymbolic machine learning techniques can be classified based on such underlying learningstrategies as rote learning, learning by being told, learning by analogy, learning from examples, andlearning from discovery [3], have been studied extensively by AI researchers over the past twodecades. Among these techniques, learning from examples, a special case of inductive learning,appears to be the most promising technique for knowledge discovery in real databases.Quinlan's ID3 decision-tree building algorithm and its variants [5, 6] are simple but powerfulalgorithms for inductive learning. ID3 takes objects of a known class, described in terms of a fixedcollection of properties or attributes, and produces a decision tree incorporating these attributes thatti

correctly classifies all the given objects. It uses an information-theoretic approach aimed atminimising the expected number of tests to classify an object. Its output can be summarised in termsof production rules. ID3 has been used successfully for various classification and predictionapplications.ID3 by design is able to deal with both categorical attributes and continuous values. Forcategorical attributes, attribute values can be easily enumerated. For continuous values, ID3performs a sweeping analysis of entropy reduction for all possible partition points (between any twoconsecutive values) and selects the partition point which provides the most entropy reduction [7]. Inessence, the algorithm performs a binary partition. In our system implementation, we developed anID3 program by adopting this original design.3.2. NEURAL NETWORKSThere has been an ascent in interest in artificial neural networks (ANN) in last years, mostly inthe area of feed-forward architectures for pattern recognition applications. The artificial neuralnetwork is a computational paradigm that differs substantially from the standard von Neumannarchitecture. Neural networks generally learn from experience instead of being expresslyprogrammed with rules as in conventional artificial intelligence. They are typically presented withtraining sets of representative instances of some classes, correctly classified, and they learn torecognise and classify other new instances of these classes. Among the computational models ofneural networks proposed, the backpropagation network has demonstrated very good capability forvarious complex classification and prediction problems [8].A backpropagation network is a multiple-layered, feed-forward neural network: each unit(neuron) in a layer is connected in the forward direction to every unit in the next layer. Knowledgeof the network is encoded in the weights between units.For more application it is impossible to present all possible data. The network needs togeneralise from the learning set in order to recognise unknown data having similar characteristics.3.3. EVOLUTIONARY ALGORITHMDuring the past two decades there has been a growing interest in applying evolutionarytechniques to machine learning. Their use is often compared with neural networks and the symboliclearning method, and their self-adaptiveness property is fairly appealing for data classificationapplications [2, 9].We used one of less known type of evolutionary algorithm i.e. evolutionary strategy (ES). Ourversion takes the algorithm for the grouping problem [11] as its model; we have not tried tooptimise any parameters of the algorithm. It is (1,) - ES where 50 children are generated from oneparent by means of the simple mutations; the crossover is not employed. The best of the descendantsbecomes the new parent solution. This manner of selection (the parent do not compete with thedescendants) often causes deterioration of the parent solution but it improves the efficiency of thealgorithm. The action of the algorithm is terminated after generating only 1 000 populations. Itmeans that only 50 000 evaluations of single solution is made during algorithm.The goal of the ES was to construct appropriate prototypes from training instances. Afterconstructing a set of prototypes (group of countries), a new input (testing) instance was determinedby the nearest prototype to this instance. Then we assumed that the average steel consumption inthis group of countries will be the best measure of the steel consumption for testing instance.

4. TESTING THE TECHNIQUES: KNOWLEDGE ACQUISITION FOR MARKETINGRESEARCH INFORMATION SYSTEM IN STEEL INDUSTRYIn this section we review the procedure applied to create the steel marketing database andidentify useful attributes. The methodology underlying our forecasting technique is based on historiclevels of steel demand given particular levels and structures of GDP. The key sectors of relevance toGDP structure were chosen as: agriculture, forestry and fishing; manufacturing industry; construction, trade.After establishing the historic data concerning the contribution of these sectors to overall GDPas well as historic per capita GDP and apparent steel demand for a number of economies in thedeveloped world over the period 1980-1997, the following database was prepared:{GDP c , Agric, Manuf, Cons, Trade, SD c }, whereGDP c - per capita GDP (in year 1995 prices);Agric - % contribution to GDP made by agriculture, forestry and fishing;Manuf - % contribution to GDP made by manufacturing industry;Const - % contribution to GDP made by the construction industry,Trade - % contribution to GDP made by the trade sector,SD c - per capita steel demand.The countries chosen for the creation of this database included Australia, Austria, Belgium,Brazil, Canada, China, Germany, France, India, Italy, Japan, Mexico, Netherlands, Spain, SouthAfrica, Sweden, Turkey, the US, the UK in the 1980, 1990, 1995, 1996 and 1997 year. The trainingset consists of 79 instances, while testing one - 6.The technique therefore predicts demand on the basis of historic per capita consumption in"testing" countries which historically had a level of per capita GDP and a GDP structure that wasmost akin to "training" countries.A typical output from this methodology might thus be that in the year 2005, Poland's level andstructure of GDP will be such that per capita steel demand should equal x kilograms. On this basis,recognising Poland's population in the year 2005, the expectation would be for Polish steel demandin 2005 to equal y million tonnes.4.1 Macroeconomic considerationsAfter the economic slump in 2001, the forecasts as for the growth of polish economy are verymoderate for the next few years. According to the Polish Ministry of Finance the GDP growth ratesof ~5% per annum will be feasible over the period 2005-2010 (after accession to the UE). The years2005-2010 are likely to witness stronger economic growth because of increasing investment anddomestic consumption as well as because of export growth. Summary of economic forecasts areshown in Table 1 below.

Table 1: Summary of economic forecasts for Poland to 2010Year 2002 2003 2004 2005-2010Growth of the GDP [%] 1.5 3.0 4.0 5.0Population [000] 38 643 38 640 38 637 38 634-38 788GDP c [1995 USD] 4 403 4 535 4 717 4 953-6 296Assumptions concerning the likely evolution of structure of polish economy were taken froma reference source [13] and the recent economic magazines; these assumptions were as shown inTable 2.Table 2: Likely contribution of different sectors of the polish economy to overall GDPYear/Contribution [%] 2005 2010Agriculture 4.9 3.6Manufacturing Industry 28.7 27.3Construction 7.0 6.7Trade 14.4 13.24.2. Experimental results and forecast for PolandWe developed the programs in Borland Pascal. Table 3 summarises the outputs predicted byID3, the backpropagation network, and the ES. As contrast the table presents the results of multipleregression analysis (MR).4.2.1. ID3All attributes in our model have continuous values. For continuous values, we used a methodthat performs a sweeping analysis of entropy reduction for all possible partition points and selectsthe partition point that provides the most entropy reduction. In this manner we transformed thevariable depicting steel consumption into categorical attributes.The advantages of ID3 include the following: it generates a ready-made set of rules, which can be directly transferred to the expert system, when this algorithm is implemented not only an object is categorised into a given class but alsothe reason for such a categorisation is provided.These advantages can be fully perceived in an analysis of some prognostic errors. The greatestdeviation ever contained concerns an instance of steel consumption testing in Poland in 1980. Asthe result of ID3 algorithm implementation it was assumed that the consumption volume should bein the interval from 250 to 300 kilograms, which was inferred in the following way:if GDP c < 7 338 thenif not Manuf < 44.5 thenif GDP c < 2 688 then Prognosed steel consumption (250,300>If GDP c were greater than 2 688 Prognosed steel consumption would be greater than 550 kg,which would be very close to the actual value. On the basis of the above, one can draw theconclusion that the teaching instance lacked an example of a country which would have similarGDP structure and such a low GDP level as Poland, and which would have such a high level of steel

consumption. Conclusions of this kind make it possible to complement the teaching instance in acorrect way or to eliminate all the incidental examples which are inconsistent with the rules of theexpert system approach.4.2.2. BACKPROPAGATION NETWORKOur backpropagation network consisted of standard 3-layer design. We tested learning rates,alpha and theta varying between 0.1 and 0.9, with 0.1 increment. Among these parameters, alearning rate of 0.4, alpha of 0.7 and theta of 0.3 were stable and converged quickly. After fixingthese parameters, we tried to find the best number of learning epochs. The tests showed that theoutputs continued to improve only a little when we increased the training epochs, so we decided tofix this parameter at 50 000 epochs.4.2.3. EVOLUTIONARY ALGORITHMThe solution was represented by a list of n countries (items) and k separators of groups; thevalue j (1 j n) determining the country number can appear in the list just once, just as the value i(n+1 i n+k) determining the number of separators. Each solution (chromosome) contains 6 genesdescribing economic factors of country development.The decoding and encoding mechanisms are very simple. For example: for 7 countries and 3separators the solution S 1 = (1,3,9,8,5,2,7,10,6,4) means that the items are divided into three groups(1,3), (5,2,7) and (6,4) when the solution S 2 = (1,10,3,8,5,2,9,7,6,4) means that the countries aredivided into four groups (1), (3), (5,2) and (7,6,4).By setting k to the value required by designer, we can control the number of separating groupsin the solutions (we set k=15 in our experiments).We computed the fitness function on a basis of the genes values. A sum of the Euclideandistance computed for every group of countries was used as a performance index. The fitnessfunction was minimised.Operators known from the literature such as exchange, insertion and inversion were applied tothe algorithm. The only difference was the fact that the moves within the groups were forbidden. Anessential assumption that makes the algorithm powerful is the fact that the items as well as theseparators were subject to the operations.No.Country, yearGDP cTable 3. Summary of experimental resultsAgric[%]Manuf[%]Cons[%]tTrade[%]Real steelcons.[kg/pc]Prognosed steel consumptionID3 ANN ES MR[1995 USD]1 Spain, 1985 10 641 8.6 44.3 9.7 29.3 147.5 126,2 158,2 112,9 214,72 Turkey, 1985 2 082 22.1 39.4 4.7 21.4 89.8 26,0 130,5 131,7 14,63 France, 1990 25 640 4.2 29.8 6.4 18.9 261.3 226,3 363,2 309,4 368,74 UK, 1993 17 893 3.4 46.7 9.5 25.4 208.7 226,3 259,7 215,8 305,45 Austria, 1995 28 775 1.9 28.7 9.0 21.6 484.7 426,6 420,2 494,0 455,46 Poland, 1996 3 491 6.4 30.1 7.4 20.9 186.4 226,3 151,1 198,5 210,9mean of relative differences [%] 23.46 24.71 16.72 39.33standard deviation of differences [%] 23.70 14.81 17.00 27.79maximum difference [%] 71.05 45.32 46.66 83.77The test results indicated the average error oscillates around 20% for all techniques, witherrors in singular forecasts exceeding as much as 45% (71% in the case of ID3).

Table 4: Probable demand forecast in Poland using artificial intelligenceTechnique ID3 ANN ESYear 2005 2010 2005 2010 2005 2010Steel Consumption per capita [kg] 426.6 426.6 195.0 314.4 198.5 198.5Total Consumption (000’s tonnes) 16 481.3 16 547.0 7 533.6 12 194.9 7 668.8 7 699.4The forecast results are ambiguous: ES assumes that there will be no significant changes insteel demand while ID3 and ANN show that polish market will increase rapidly. One of the possibleexplanation is fact that set of training data was very small. It follows that applying these techniquesrequires very careful training data preparation.5. CONCLUSIONThis paper shows that ID3, backpropagation network and evolutionary strategy can beeffective means for addressing supervised classification problems. This paper contains anexperimental evaluation of the above mentioned techniques on a set of real-valued marketingresearch problem.In terms of prediction accuracy, both the backpropagation network and ID3 performed worsethan the evolutionary algorithm. Evolutionary strategy appeared to be more robust than other twoalgorithms in their ability to analyse the large set of marketing data objectively and reach unbiasedconclusions. ID3's decision tree output was more understandable than that of the backpropagation.In general, ID3 also predicted more conservatively than other approaches. The backpropagationnetwork, on the other hand, was less computationally expensive (and thus very quick).In summary, this research has examined in detail the characteristics and feasibility of variousprediction techniques, including symbolic learning (ID3), a connectionist approach(Backpropagation), and evolutionary clustering (ES) for complex marketing problem. For large reallifedatabases of historical information, recent machine learning techniques appeared to exhibitunique capabilities for data analysis and knowledge discovery.While the results of our research have confirmed the practicality and value of the mentionedtechniques for building marketing expert systems, it is also evident that further research in this areais necessary.REFERENCES[1] Evans M., Modelling steel demand in the UK, Ironmaking and Steelmaking, Vol.23 (1996),No.1.[2] Knight L., Sen S., PLEASE: A prototype learning system using genetic algorithm,[3] Carbonell J., G., Michalski R. S., and Mitchell T. M., An overview of machine learning,Machine Learning, an Artificial Intelligence Approach, Tioga Publishing Company, Palo Alto1983, pp. 3-23.[4] Malenbaum, World demand for raw materials in 1985 and 2000, McGraw-Hill BookCompany, New York 1975.[5] Quinlan J. R., Learning efficient classification procedures and their application to chess endgames, Machine Learning, an Artificial Intelligence Approach, Tioga Publishing Company,Palo Alto 1983, pp. 463-482.[6] Quinlan J. R., Induction of decision trees, Machine Learning, (1986), No.1, pp. 81-106.[7] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kauffmann, Los Altos 1993.

[8] Reeves C.R., Modern heuristic techniques for combinatorial problems, McGraw-Hill BookCompany, Berkshire 1995.[9] Schwefel H.P., Evolution and optimum seeking, John Wiley & Sons, New York 1995.[10] Scott G. M., Principles of Management Information Systems, McGraw-Hill Book Company,New York 1986.[11] Stawowy A., Evolutionary algorithm for grouping parts problem, [in] Management – theoryand practice. Faculty of Management, Krakow 1999.[12] Steel market evolution in Poland. Report to the European Commission, Beddows &Company, London 1998.[13] Welfe W; Welfe A., Florczak W., Long term forecasts for Polish Economic Development to2010, Gospodarka Narodowa, (1997), No. 11-12.

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