経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

‫ػ‬ցֶशʹΑΔΦʔτΦʔΫγϣϯམࡳՁ֨ͷ༧ଌ
Price Prediction of Used Cars at Auto Auctionby Machine Learning MethodsᓎҪ େ஦ 1∗ ଎‫ ݟ‬༐ຏ 1 ࢁԼ སࠬ 1 ௕୩઒ ‫ܙ‬ཧࢠ 2Լࢁ ྗࡾ 2 ෱੢ ྄հ 2 ୃ ޿थ 2 ླ໦ ஐ໵ 1, 3
Hiromichi Sakurai1 , Yuma Hayami1 , Risa Yamashita1 , Eriko Hasegawa2 ,Rikizou Shimoyama2 , Ryosuke Fukunishi2 , Hiroki Mayuzumi2 and Tomoya Suzuki1, 311ҵ৓େֶେֶӃཧ޻ֶ‫ڀݚ‬ՊGraduate School of Science and Engineering, Ibaraki University2‫ࣜג‬ձࣾϓϩτίʔϙϨʔγϣϯ2PROTO CORPORATION
3ίϥϘ΢Οζ‫ࣜג‬ձࣾ, 3 CollabWiz, Inc.

Abstract: Buyers of used cars have to predict their dealing prices at auto auction held after
about one month, but it is very diﬃcult to predict them because each condition of used cars iscompletely diﬀerent such as mileage, model year, body color, etc. For this reason, we propose
two prediction methods: as the first one, we consider the median of dealing prices in each car
model as a base price and predict its future price by time-series models: ARIMA and SARIMA.
After that, the predicted bace price is converted into the individual price of each used car by themachine learning method that learned the relationship between the condition of used cars including
individual prices and the bace price. As the second method, we adopt the deep learning approachto directly predict individual future prices of used cars without using the base price, but using all
the information attached to each used car as explanatory variables. To verify the usefulness of our
proposed methods for a used car assessment system, we performed some prediction tests using thereal auto-auction price data.

1͸͡Ίʹத‫ंݹ‬͸Ϣʔβʔ͔Βങऔళ΁ྲྀΕͨ‫ޙ‬ɼओʹΦʔτΦʔΫγϣϯ [1] Λ௨ͯ͡ൢചళʹച٫͞ΕΔɽങऔళͷཱ৔ʹཱͯ͹ɼങऔ͔ΒΦʔτΦʔΫγϣϯʹग़඼͢Δ·ͰҰϲ݄͸ཁ͢ΔͨΊɼཌ݄ͷ‫ڝ‬ങՁ֨Λ༧૝ͯ͠ങऔࠪఆՁ֨Λઃఆ͢Δඞཁ͕͋Δɽͦ͜Ͱ‫ג‬ࣜձࣾϓϩτίʔϙϨʔγϣϯ (ҎԼɼϓϩτࣾ) Ͱ͸ɼཌ݄ʹ։࠵͞ΕΔΦʔτΦʔΫγϣϯͰͷམࡳՁ֨Λߟྀͨࠪ͠ఆγεςϜΛ੡࡞͠ɼத‫ंݹ‬ങऔళ͕ࠪఆՁ֨Λܾఆ͢Δࡍͷ৘ใఏ‫ڙ‬Λߦ͍ͬͯΔɽ͔͠͠த‫ंݹ‬ͷྲྀ௨Ձ֨Λ༧ଌ͢Δ͜ͱ͸େม೉͍͠ɽͦͷཧ༝ͱͯ͠ɼத‫ंݹ‬͸૸ߦ‫཭ڑ‬΍ं‫ͱ݄࢒ݕ‬͍ͬͨಛ௃͕‫ʹͱ͝ंݸ‬ҟͳΓɼ།ҰແೋͷଘࡏͰ͋Δɽͦ͜ͰϓϩτࣾͷࠪఆγεςϜ [2] Ͱ͸ɼ·ͣର৅‫ͱंݸ‬ಉҰंछͷམࡳ࣮੷Λ౿·͑ɼཌ݄ͷ֘౰ंछ∗ ࿈བྷઌɿ ҵ৓େֶେֶӃཧ޻ֶ‫ڀݚ‬Պ ҵ৓‫ݝ‬೔ཱࢢத੒୔ொ
4-12-1 E-mail: 17nm920a@vc.ibaraki.ac.jpͷࢦఆ৚݅ͷՁ֨ʢ୅දՁ֨ʣΛਪ࿦͠ɼ͞Βʹର৅‫ंݸ‬ͷঢ়ଶʢઆ໌ม਺ʣʹ‫ंݸ͍ͯͮج‬ͷམࡳՁ֨΁ม‫͢׵‬Δͱ͍ͬͨɼ2 ஈ֊ͷϓϩηεΛ‫ܦ‬Δɽ֤ϓϩηεͰ͸ओʹύϥϝʔλΛ༩͑ͯ‫ػ‬ցతʹਪ࿦͢Δ͕ɼෆ߹ཧͳ݁ՌΛగਖ਼͢ΔͨΊʹਓ͕ؒ༗͢Δॊೈͳਪ࿦ೳྗΛ‫͠࢖ۦ‬ɼϑϨʔϜ໰୊ʹରॲ͢Δ৔߹΋͋Δɽ͔͠͠ຊ‫Ͱڀݚ‬͸ɼશͯ‫ػ‬ցతʹਪ࿦͢Δ͜ͱͰશࣗಈ͔ͭओ‫͕؍‬հࡏ͠ͳ͍༧ଌγεςϜΛߏங͍ͨ͠ɽ·ͣ͸ϓϩτࣾͷΑ͏ʹ 2 ஈ֊ͷϓϩηεʹ෼ׂ͠ɼ୅දՁ֨ͷ༧ଌʹ͸࣌‫ྻܥ‬ϞσϧΛɼ‫ंݸ‬Ձ֨΁ͷม‫ʹ׵‬͸‫ػ‬ցֶशϞσϧΛద༻͢Δɽ͔͠͠୅දՁ֨ͷ༧ଌ‫͕ͦࠩޡ‬ͷ‫ޙ‬ͷม‫ʹ׵‬ѱӨ‫ڹ‬Λ‫ͨ͢΅ٴ‬Ίɼ‫ंݸ‬ͷམࡳՁ֨Λ௚઀༧ଌ͢Δਂ૚ֶशϞσϧʹ͍ͭͯ΋‫ݕ‬౼͢Δɽ௚઀༧ଌͰ͸‫ंݸ‬ͷ༗͢Δશಛ௃ྔΛઆ໌ม਺ͱ͢ΔͨΊߴ࣍‫ݩ‬ͷ‫ػ‬ցֶशͱͳΔ͕ɼੵ૚ΦʔτΤϯίʔμΛࣄલֶशʹద༻͢Δ͜ͱͰ࣍‫ݩ‬ѹॖʢಛ௃બ୒ʣΛߦ͏ɽ

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)2໰୊ઃఆͱ༻͍Δσʔλத‫ंݹ‬औҾͷྲྀΕ͸࣍ͷ 4 ͭʹ෼ྨ͞ΕΔɽ(1) ങऔళ͕‫ސ‬٬͔Βத‫ंݹ‬Λങ͍औΔɼ(2) ങ͍औͬͨத‫ंݹ‬Λങऔళ͕ΦʔτΦʔΫγϣϯʹग़඼͢Δɼ(3) ൢചళ͕த‫ंݹ‬Λམࡳ͢Δɼ(4) ൢചళ͕‫ސ‬٬ʹத‫ंݹ‬Λച٫͢Δɽຊ‫Ͱڀݚ‬͸த‫ंݹ‬ങऔళͷཱ৔ʹཱͪɼ(3) ͰͷམࡳՁ֨Λߟྀͭͭ͠ɼ(1) ʹͯ߹ཧతͳങऔՁ֨ΛܾΊΔͨΊͷํ๏࿦Λ‫ݕ‬౼͢Δɽ΋͠ങऔՁ֨Λߴ͘‫ݟ‬ੵ΋Ε͹ങऔళͷརӹ͕‫ݮ‬Γɼ௿͘‫ੵݟ‬΋Ε͹‫ސ‬٬͕ଞళʹྲྀΕΔՄೳੑ͕͋Δɽ·ͨങ͍औΓ͔ΒΦʔτΦʔΫγϣϯ΁ͷग़඼·Ͱ੔උ౳ʹҰि͔ؒΒҰϲ݄ఔ౓ͷλΠϜϥάΛཁͨ͢Ίɼ(1) ͷ࣌఺Ͱ (3) ͰͷམࡳՁ֨Λ༧ଌ͢Δඞཁ͕͋Δɽຊ‫ڀݚ‬ͷ༧ଌର৅ͱͯ͠ɼʮημϯʯʮίϯύΫτʯʮϛχόϯʯʮ̨̪̫ʯʮܰࣗಈंʯͷ 5 ΧςΰϦ͔Βɼʮ̡̨ʯʮϓϦ΢εʯʮϑΟοτʯʮσϛΦʯʮηϨφʯʮύδΣϩʯʮϑΥϨελʔʯʮλϯτʯʮϫΰϯ̧ʯͷ 9 ंछΛબग़ͨ͠ɽϓϩτ͕ࣾ༗͢ΔΦʔτΦʔΫγϣϯσʔλ͸ɼΦʔτΦʔΫγϣϯձ৔͕‫ंݸͨࠪ͠ݕ‬ͷঢ়ଶΛද͢ଟมྔσʔλͰߏ੒͞Ε͍ͯΔɽͦͷҰྫͱͯ͠ʮམࡳՁ֨ʯʮ૸ߦ‫཭ڑ‬ʯʮं‫ݕ‬ຬྃ೥݄ʯʮम෮ྺʯʮ಺૷ධՁʯͳͲ͕‫͛ڍ‬ΒΕΔɽ·ͨϚελʔσʔλͱͯ͠ɼʮंछʯʮϞσϧʯʮ‫ࣜܕ‬ʯʮάϨʔυʯʹԠͨ͡ʮΩʔίʔυʯʮެশੑೳʯʮΦϓγϣϯ૷උʯʮ৽ंՁ֨ʯʹؔ͢Δ৘ใ΋ར༻Ͱ͖Δɽ͜ͷΩʔίʔυ͕ࣗಈंΛ෼ྨ͢Δ࠷খ୯ҐͰ͋ΔͨΊɼΦʔτΦʔΫγϣϯσʔλ͔ΒΩʔίʔυΛඥ෇͚ͯϚελʔσʔλ͔Β৘ใΛநग़͢Δɽ

3୅දՁ֨ͷ࣌‫ྻܥ‬༧ଌ

3.1࣌‫ྻܥ‬༧ଌϞσϧͷద༻ΦʔτΦʔΫγϣϯʹͯத‫͕ंݹ‬མࡳ͞ΕΔͷ͸ങऔళ͕ࠪఆങऔ͔ͯ͠ΒҰϲ݄ఔ౓ઌͰ͋Δ͜ͱ͕ଟ͘ɼങऔళ͸த‫ंݹ‬Λࠪఆ͢Δͱ͖ʹཌ݄ͷམࡳՁ֨Λ༧ଌ͢Δඞཁ͕͋Δɽ͜ͷ༧ଌʹ͸ࠪఆ౰݄·ͰͷΦʔΫγϣϯམࡳ৘ใΛࢀߟʹ͢Δ͕ɼલड़ͷͱ͓Γର৅ं྆ͱಉҰ৚݅ͷա‫੷࣮ڈ‬͸ଘࡏ͠ͳ͍ͨΊɼ౷‫ܭ‬తʹਪ࿦͢Δ͜ͱ͕Ͱ͖ͳ͍ɽͦ͜ͰຊষͰ͸ॆ෼ͳαϯϓϧ਺Λ֬อ͢ΔͨΊʹɼ‫Ͱंݸ‬͸ͳ͘ंछຖʹམࡳՁ֨ΛτϥοΩϯά͢Δɽ·ͨंछͷ୅දՁ֨ͱͯ͠ɼམࡳՁ֨ͷதԝ஋Λ‫ػ‬ցతʹ༻͍Δɽ࣍ʹɼཌ݄ͷ୅දՁ֨ʢதԝ஋ʣΛ༧ଌ͢ΔͨΊʹɼա‫ڈ‬ͷ୅දՁ֨Λ༻͍ͯ࣌‫ྻܥ‬ϞσϧΛֶश͢Δɽத‫ंݹ‬͸ྲྀߦͳͲͷʮτϨϯυཁҼʯ΍৽ֶ‫ظ‬धཁͳͲͷʮ‫ق‬અੑཁҼʯ͕Ө‫͢ڹ‬Δͱߟ͑ΒΕΔɽ͜ΕΒͷඇఆৗཁҼʹର͢Δ࣌‫ྻܥ‬Ϟσϧͱͯ͠ɼຊ‫Ͱڀݚ‬͸ࣗ‫ݾ‬ճ‫ؼ‬࿨෼Ҡಈฏ‫( ۉ‬ARIMA) Ϟσϧͱ‫ق‬અࣗ‫ݾ‬ճ‫ؼ‬࿨෼Ҡಈฏ‫( ۉ‬SARIMA) ϞσϧΛద༻͢Δɽ

࣌ࠁ t ʹ͓͚Δ͋Δंछͷ୅දՁ֨ʢதԝ஋ʣΛ Xtͱॻ͘ͱɼARMA(p, q) Ϟσϧ͸࣍ࣜͱͳΔɽ

Xt =p!

ai Xt−i + ϵt +i=1q!

bi ϵt−i(1)i=1͜͜Ͱɼai ͸ա‫ڈ‬ͷ࣮‫ݱ‬஋ Xt−i ʹ͓͚Δ࣌ؒ‫ه‬Աྗɼbi ͸ա‫ڈ‬ͷϗϫΠτϊΠζ ϵt−i ʹ͓͚Δ࣌ؒ‫ه‬ԱྗΛ
ࣔ͢ɽARMA Ϟσϧ͸ఆৗੑΛԾఆ͢ΔͨΊɼ্‫ه‬ͷඇఆৗཁҼΛද‫͍ͳ͖Ͱݱ‬ɽXt ͷτϨϯυཁҼʹ͍ͭͯ d ֊ࠩ෼ΛͱΓɼ‫ق‬અੑཁҼʹ͍ͭͯ D ֊‫ق‬અࠩ෼ΛऔΔ͜ͱͰఆৗԽॲཧΛࢪ͢ɽͳ͓ Xt ͸݄࣍σʔλͰ͋ΔͨΊɼ‫ق‬અप‫ظ‬͸ s = 12ʢ12ϲ݄ʣͱ͢Δɽͦͷ‫ޙ‬ɼఆৗԽࡁΈ ∆d ∆Ds Xt ʹ͍ͭͯࣜ 1 ͷ ARMAϞσϧΛద༻͠ɼਪఆ͞Εͨ ∆d ∆Ds X̂t+1 Λ‫͢ࢉٯ‬Δ͜ͱͰ X̂t+1 ΛಘΔɽࣜ 1 Ͱ͸࣌ؒ‫ؔ͢ʹྻܥ‬Δ‫ه‬ԱੑΛද‫͖Ͱݱ‬ΔͨΊɼARIMA(p, d, q) Ϟσϧͱ౳ՁͰ͋Δɽ͔͠͠‫ق‬અप‫ ظ‬s Λ·͙ͨ࣌ؒ‫ه‬Աੑ΋ද‫͢ݱ‬Δʹ͸ɼ࣍ࣜͷ SARIMA(p, d, q)(P, D, Q)[s] Ϟσϧ [3] Λ༻͍Δɽ

∆d ∆Ds Xt =p!

a i ∆d ∆ Ds Xt−i +i=1−p !
P!

P!

AI ∆ d ∆ Ds Xt−sI

I=1da i AI ∆ ∆ D
s Xt−sI−i + ϵt +i=1 I=1+q!

bj ϵt−jj=1Q!

BJ ϵt−sJ +q !
Q!

bj BJ ϵt−sJ−j (2)j=1 J=1J=1্‫ͱه‬ಉ༷ʹɼਪఆ͞Εͨ ∆d ∆Ds X̂t+1 Λ‫͢ࢉٯ‬Δ͜ͱͰ X̂t+1 ΛಘΔɽͨͩ͠Ϟσϧߏ଄͕ෳࡶͰ͋ΔͨΊɼֶशʹ൐͏σʔλෆ଍͓Αͼաֶशͷ໰୊͕‫ݒ‬೦͞ΕΔɽϞσϧ࣍਺ʹ͍ͭͯ͸ AIC Λ࠷খԽ͢ΔΑ͏ʹܾఆ͢Δ͕ɼաֶशͷ‫͔఺؍‬ΒΑΓγϯϓϧͳ ARIMAϞσϧͷํ͕‫ػ‬ೳ͢ΔՄೳੑ΋͋Δɽ

3.2

༧ଌ࣮‫ݧ‬

2011 ೥ 10 ݄͔Β༧ଌର৅ͷલ݄·Ͱͷ୅දՁ֨Λ༻͍ͯ࣌‫ྻܥ‬ϞσϧΛΦϯϥΠϯֶश͠ɼ2016 ೥ 1 ݄
͔Β 2017 ೥ 9 ݄Λ༧ଌର৅ͱͨ͠ɽ༧ଌਫ਼౓ͷධՁʹ͸ɼਖ਼‫ن‬Խฏ‫ۉ‬ೋ৐‫( ࠩޡ‬NMSE) ͱਖ਼౴݄਺Λ༻͍ͨɽNMSE ͸࣍ࣜͰಘΒΕΔɽ
"# T#!
$ (Xt − X̂t )2
t=1

NMSE(X̂) = "# T#!
$ (Xt − X̄)2
t=1(3)

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ද 1: ARIMA ϞσϧʹΑΔ୅දՁ֨ͷ༧ଌ݁Ռ
ΧςΰϦ

ंछ

NMSEਖ਼౴݄਺

ημϯημϯίϯύΫτίϯύΫτϛχόϯ
̨̨̪̫̪̫ܰࣗಈंܰࣗಈं̡̨ϓϦ΢εϑΟοτσϛΦηϨφύδΣϩ
ϑΥϨελʔ
λϯτ
ϫΰϯ̧

0.559
0.621
0.552
0.546
0.629
1.613
1.062
0.454
0.913

17
19
15
1418
14
91915

ද 2: SARIMA ϞσϧʹΑΔ୅දՁ֨ͷ༧ଌ݁Ռ
ΧςΰϦ

ंछ

NMSEਖ਼౴݄਺

ημϯ
ημϯ
ίϯύΫτ
ίϯύΫτ
ϛχόϯ
̨̪̫
̨̪̫
ܰࣗಈं
ܰࣗಈं

̡̨
ϓϦ΢ε
ϑΟοτ
σϛΦ
ηϨφ
ύδΣϩ
ϑΥϨελʔ
λϯτ
ϫΰϯ̧

0.466
0.797
0.631
0.515
0.666
1.127
1.004
0.451
0.850

2018151518
17
11
1917

͜͜ͰɼT ͸༧ଌର৅ͷ‫ؒظ‬ͷ௕͞ɼXt ͸࣌ࠁ t ʹ͓͚Δ୅දՁ֨ͷਅ஋ɼX̂t ͸࣌ࠁ t ʹ͓͚Δ୅දՁ֨ͷ
༧ଌ஋ɼX̄ ͸‫ ؒظ‬t = 1, 2, · · · , T ʹ͓͚Δ Xt ͷฏ‫ۉ‬஋Λࣔ͢ɽਖ਼౴݄਺͸ɼX̂t ͕ Xt ͷ ±10% ʹऩ·ͬͨ৔߹Λਖ਼ղͱΈͳ͠ɼ༧ଌର৅ͷ 21ϲ݄ؒ (T = 21)ͷ͏ͪਖ਼ղ݄ͨ͠਺Λࣔ͢ɽ༧ଌ݁ՌΛද 1,2 ͓Αͼਤ 1∼3 ʹࣔ͢ɽημϯ͸
ARIMA Ϟσϧͱ SARIMA Ϟσϧͷ྆ํʹ͓͍ͯਖ਼౴݄਺͕ଟ͘ɼNMSE ΋௿͍஋ͱͳͬͨɽཧ༝ͱͯ͠ɼ
ημϯͷτϨϯυ͸ൺֱతམͪண͍͓ͯΓɼ‫ق‬અੑʹ͍ͭͯ΋มಈ෯͕ଞंछΑΓখ͍͞ɽҰํɼझຯੑ͕ߴ̨͍̪̫͸τϨϯυมԽ͕ܹ͘͠ɼ‫ق‬અੑมಈ෯΋େ͖͍ͨΊ༧ଌ͕೉͍͠ɽͦͷ݁Ռɼਖ਼౴݄਺͸গͳ
͘ɼNMSE ΋ߴ͍܏޲ʹ͋Δɽಛʹਤ 2 ʹ͓͍ͯɼ༧
ଌ஋͕ਅ஋Λ 1ϲ݄஗Εͯ௥͍͔͚͍ͯΔ‫ݟ͕ؒظ‬Β
ΕΔɽͳ͓ ARIMA Ϟσϧͱ SARIMA Ϟσϧͷൺֱʹ͓͍ͯ͸ɼಛ‫͢ه‬Δ΄Ͳͷࠩ͸‫ݟ‬ड͚ΒΕͳ͍ɽਤ 1: SARIMA ʹΑΔϓϦ΢εͷ୅දՁ֨ͷ༧ଌਪҠਤ 2: SARIMA ʹΑΔύδΣϩͷ୅දՁ֨ͷ༧ଌਪҠਤ 3: SARIMA ʹΑΔϫΰϯ̧ͷ୅දՁ֨ͷ༧ଌਪҠ

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

4‫ػ‬ցֶशʹΑΔ‫ंݸ‬Ձ֨΁ͷม‫׵‬4.1ϥϯμϜϑΥϨετͷద༻࣍ʹɼ୅දՁ֨ (தԝ஋) ͷ༧ଌ஋Λ‫ंݸ‬Ձ֨΁ม‫׵‬͢Δํ๏Λ‫ݕ‬౼͢Δɽ‫ंݸ‬ͷಛ௃ྔͱ୅දՁ֨ͷ༧ଌ஋Λઆ໌ม਺ͱ͠ɼ‫ंݸ‬ͷམࡳՁ֨Λඃઆ໌ม਺ʹ͢Ε͹ɼ྆ม਺ͷؔ܎Λա‫ڈ‬ͷ࣮੷ʹ‫ػ͍ͯͮج‬ցֶश͢Ε͹ྑ͍ɽͦͷ‫ػ‬ցֶशϞσϧͱͯ͠ɼຊষͰ͸ϥϯμϜϑΥϨετ [4] Λ༻͍Δɽͦͷֶशखॱͱͯ͠ɼ·ͣ N ‫ݸ‬ͷֶशσʔλ (xi , yi ),(i = 1, 2, · · · , N ) ͔Β෮‫ݩ‬நग़͠ɼಉαΠζ N ͷσʔλू߹ W (b) Λ B ૊ੜ੒͢Δɽ࣍ʹɼͦΕͧΕͷσʔλू߹ W (b) , (b = 1, 2, · · · , B) Λ༻͍ͯճ‫ؼ‬໦ RT (b)Λֶश͢Δɽ ճ‫ؼ‬໦͸୯ମͰ΋࿈ଓతͳඃઆ໌ม਺ͷ༧ଌʹద༻Ͱ͖Δ͕ɼϥϯμϜϑΥϨετ͸શͯͷճ‫ؼ‬໦ͷ༧ଌ஋Λฏ‫ۉ‬Խ͢Δ͜ͱͰ༧ଌੑೳΛߴΊΔΞϯαϯϒϧֶश [5] ͷҰछͰ͋Δɽ֤ճ‫ؼ‬໦ͷಠཱੑ͕ߴ͍΄Ͳ༧ଌੑೳ͕ߴ·Δ͜ͱ͸ू߹஌ͷ‫఺؍‬ʢଟ༷ੑ༧ଌఆཧʣ͔Βઆ໌Ͱ͖Δ [6]ɽ4.2

ද 3: ϥϯμϜϑΥϨετʹΑΔ‫ंݸ‬མࡳՁ֨ͷਪఆ݁ՌΧςΰϦंछਖ਼౴཰

ܾఆ܎਺ R2ημϯημϯίϯύΫτίϯύΫτϛχόϯ̨̨̪̫̪̫
ܰࣗಈंܰࣗಈं̡̨ϓϦ΢εϑΟοτσϛΦηϨφύδΣϩϑΥϨελʔ
λϯτ
ϫΰϯ̧

0.30680.43380.23320.32220.31860.32910.40760.47240.42690.88020.89210.89330.94330.93280.9122
0.95490.9516
0.9454ม‫ݧ࣮׵‬

ֶशσʔλͷ‫ؒظ‬͸ 2016 ೥ 1 ݄͔Β 2016 ೥ 12 ݄·Ͱɼςετσʔλͷ‫ؒظ‬͸ 2017 ೥ 1 ݄͔Β 2017 ೥
9 ݄·Ͱͱ͢Δɽ·ֶͣशσʔλΛ༻͍ͯϥϯμϜϑΥϨετΛߏங͢Δɽͦͷࡍʹ͸ɼ‫ंݸ‬৘ใͱͯ͠ʮंମ৭ʯʮ೥མͪ౓ʯʮ૸ߦ‫཭ڑ‬ʯʮं‫݄࢒ݕ‬ʯʮධՁ఺ʯʮ৽ंՁ֨ʯʮφϏͷ༗ແʯʮϨβʔγʔτͷ༗ແʯʮαϯϧʔϑͷ༗ແʯΛ༻͍ɼ͞Βʹ౰֘ंछͷ୅දՁ֨ʢதԝ஋ʣΛઆ໌ม਺ͱ͢Δɽඃઆ໌ม਺ʹ͸ಉं྆ͷʮམࡳՁ֨ʯͱ͢Δ͜ͱͰɼंछຖʹ୅දՁ͔֨Β‫ंݸ‬ͷམࡳՁ֨ʹม‫͢׵‬ΔϧʔϧΛֶश͢Δɽͦͷ‫ޙ‬ɼ୅දՁ֨Λ 3 ষͰಘͨ༧ଌ஋ʹมߋ͠ɼ͜ΕΛֶशࡁΈϥϯμϜϑΥϨετʹೖྗ͢Δ͜ͱͰɼ‫ंݸ‬ͷམࡳՁ֨ͷਪఆ஋ΛಘΔɽ͜ͷਪఆ͸ςετσʔλʹର࣮ͯ͠ࢪ͢Δɽਪఆਫ਼౓ΛධՁ͢Δࢦඪͱͯ͠ɼਖ਼౴཰ͱܾఆ܎਺Λ༻͍Δɽਖ਼౴཰͸ɼਪఆ஋ ŷn ͕ਅ஋ yn ͷ ±10% Ҏ಺ʹऩ·ͬͨ৔߹Λਖ਼ղͱ͠ɼͦͷճ਺Λਪఆ࣮ࢪճ
਺ N Ͱׂͬͨ΋ͷͰ͋Δɽ͞Βʹܾఆ܎਺ R2 ͸࣍ࣜͰಘΒΕΔɽ
N!

(yn − ŷn )2R2 = 1 − n=1N!

n=1(yn − ȳ)

ਤ 4: ‫ंݸ‬མࡳՁ֨ͷਅ஋ͱਪఆ஋ͷ૬ؔਤ (ϓϦ΢εͷ৔߹)(4)2

͜͜Ͱ ȳ ͸ਅ஋ yn ͷฏ‫ۉ‬஋Ͱ͋Δɽܾఆ܎਺ R2 ͸ 1ʹ͍ۙ΄Ͳճ‫ؼ‬໰୊ͱͯ͠౰ͯ͸·Γ͕ྑ͍͜ͱΛҙຯ͢Δɽਤ 5: ‫ंݸ‬མࡳՁ֨ͷਅ஋ͱਪఆ஋ͷ૬ؔਤ (ύδΣϩͷ৔߹)経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)
JSAI Special Interest Group on Business Informatics (SIG-BI #10)

ਪఆ݁ՌΛද 3 ͓Αͼਤ 4,5 ʹࣔ͢ɽ͍ͣΕͷंछ΋ܾఆ܎਺ R2 ͸ 0.9 ʹ͍ۙ஋Λ͓ࣔͯ͠Γɼճ‫ؼ‬໰୊ͱͯ͠ͷਪఆਫ਼౓͸ߴ͍ɽ͔͠͠ਖ਼౴཰͸ 0.30 લ‫ͱޙ‬௿͘ɼਅ஋ͷ ±10% Ҏ಺Λਖ਼ղͱΈͳ͢‫ج‬४͸ඇৗʹ‫͍͠ݫ‬ɽ·ͨਤ 4 ͱਤ 5 ʹࣔ͢Α͏ʹɼ௿Ձ֨ଳʹ͓͍ͯ‫ޡ‬͕ࠩେ͖͍܏޲ʹ͋Δɽ͜ͷཧ༝ͱͯ͠ɼάϨʔυ΍೥ࣜͳͲͰ෼ྨͤͣʹֶशͨͨ͠ΊɼߴՁ֨ଳͱ௿Ձ֨ଳͷֶश‫͕ࠩޡ‬ಉҰʹѻΘΕͨͨΊͱߟ͑ΒΕΔɽਅ஋ͱ‫ࠩޡ‬ͷൺ཰Λߟ͑Ε͹ɼ௿Ձ֨ଳ͸͜ͷൺ཰͕େ͖͘ͳΓ΍͍͢ɽ͜Ε͕ਖ਼౴཰ΛԼ͛ΔཁҼʹͳΔͨΊɼࠓ‫ޙ‬͸ઈର‫͘ͳͰࠩޡ‬ʮ‫཰ࠩޡ‬ʯΛ࠷খԽ͢ΔΑ͏ʹϞσϧύϥϝʔλΛ‫ػ‬ցֶश͢Δํ๏Λ‫ݕ‬౼͢Δɽ·ͨɼάϨʔυ΍೥ࣜͳͲͰֶशσʔλΛ෼ׂ͢Δํ๏΋༗ޮ͔΋͠Εͳ͍͕ɼֶशσʔλ਺ͷ‫ݮ‬গʹΑΓ‫ػ‬ցֶशࣗମͷੑೳ͕௿Լ͢ΔՄೳੑ΋͋Δɽ‫ࠩޡ‬Λ‫ ਺ؔࠩޡ‬E ͱ͠ɼֶशσʔλΛϥϯμϜʹ 1%બͿόονֶशΛ 10000 ճ‫܁‬Γฦ͢ɽޯ഑߱Լ๏ͷ࠷దԽΞϧΰϦζϜʹ͸ Adam[10] Λ༻͍ɼ֤ύϥϝʔλΛ α = 0.001ɼβ1 = 0.9ɼβ2 = 0.999 ͱͨ͠ɽϑΝΠϯνϡʔχϯάʹ͓͍ͯ͸ޯ഑ফࣦ໰୊ʹΑΓೖྗ૚෇ۙͷॏΈ͸ߋ৽͞Εʹ͍͕͘ɼࣄલֶशʹ͓͍ͯ༏ઌతʹֶश͞Ε͍ͯΔͨΊॏେͳ໰୊ͱ͸ͳΒͳ͍ɽ

5.2௚઀༧ଌ࣮‫ݧ‬ֶशσʔλͱςετσʔλͷ‫ؒظ‬͸ 4.2 અͱಉ༷Ͱ
͋Δɽઆ໌ม਺ͱͯ͠ʮंମ৭ʯʮ೥མͪ౓ʯʮ૸ߦ‫ڑ‬཭ʯʮं‫݄࢒ݕ‬ʯʮධՁ఺ʯʮ৽ंՁ֨ʯʮφϏͷ༗ແʯʮϨβʔγʔτͷ༗ແʯʮαϯϧʔϑͷ༗ແʯΛ༻͍ɼʮ‫ंݸ‬མࡳՁ֨ʯΛඃઆ໌ม਺ͱ͢Δɽ4.2 આͱͷҧ͍͸ɼ୅දՁ֨Λઆ໌ม਺ʹ‫ؚ‬Ίͳ͍఺ͱɼඃઆ໌ม਺ͷ஋Λʮࠪఆ࣌఺ʹ໭ͯ͠ʯֶश͢Δ఺ʹ͋Δɽྫ͑͹ɼ೥མͪ౓͸༧ଌର৅‫ंݸ‬ͷ೥ࣜͱΦʔτΦʔΫγϣϯ։࠵
5 ਂ૚ֶशʹΑΔ݄ͷࠩΛҙຯ͢Δɽͦ͜Ͱ೥མͪ౓Λ 1ϲ݄Ҿ͍ͯࠪఆ࣌఺ͷ৘ใΛਂ૚ֶशʹೖྗ͢Δ͜ͱͰɼ1ϲ݄ઌͷ‫ंݸ‬མࡳՁ֨ͷ௚઀༧ଌΦʔΫγϣϯ։࠵࣌఺ͷམࡳՁ֨ (༧ଌ஋) Λग़ྗ͢Δɽ
5.1 ਂ૚ֶशͷద༻͞Βʹ֤ม਺ͷεέʔϧௐ੔ (લॲཧ) ͱͯ͠ɼʮंମ৭ʯ͸ंछຖͷग़‫ͯ͠ͱ཰ݱ‬ఆྔԽ͠ɼ৭ͷՁ஋ (ਓ3 ষͱ 4 ষʹΑΔ༧ଌϓϩηεʹ͓͍ͯɼ‫ൃ͕ࠩޡ‬ੜ‫ )౓ؾ‬Λද͢ɽʮ೥མͪ౓ʯ΍ʮ૸ߦ‫཭ڑ‬ʯ͸ंछຖʹ͢ΔཁҼ͸ 3 ఺͋Δɽ·ͣंछຖͷ୅දՁ֨ͱͯ͠த
ֶशσʔλͷશं྆ͷؒͰඪ४Խ͢Δɽʮं‫݄࢒ݕ‬ʯ͸ԝ஋͕ଥ౰ͳͷ͔ٙ໰͕࢒Δɽ͔͠͠‫ػ‬ցతʹ୅දՁ௨ৗຬ‫ ͕ظ‬2ϲ೥ͳͷͰࠪఆ࣌఺ʹ͓͚Δं‫݄࢒ݕ‬Λ֨Λ‫ٻ‬ΊΔϧʔϧΛఆΊͯ΋ɼͦͷϧʔϧ͕‫׬‬શʹਖ਼
24 Ͱআ͓ͯ͘͠ɽͳ͓৽ंͷॳं‫ݕ‬͸ 3ϲ೥ͳͷͰ஋͍͠ͱ͸‫ݶ‬Βͳ͍ɽ࣍ʹ୅දՁ֨ʹର͢Δ࣌‫ྻܥ‬༧ଌ
͸ 0 ∼ 1.5 ΛͱΓ͏Δɽ
ʮධՁ఺ʯ͸ΦʔτΦʔΫγϣʹ͍ͭͯ΋༧ଌ‫͕ࠩޡ‬ੜ͡ɼ͞Βʹ‫ंݸ‬མࡳՁ֨΁ͷϯձ৔ʹΑΔՁ஋ධՁͰ͋Γɼ৽‫ंݹ‬͸ 2000 ఺ɼத‫ݹ‬ม‫͍͓ͯʹ׵‬΋ม‫͕ࠩޡ׵‬ੜ͡Δɽͦ͜ͰຊষͰ͸ɼ͜ं͸ 300 ఺ ∼900 ఺Ͱ࠾఺͞ΕΔɽͦͷͨΊ‫ݩ‬ͷධՁΕΒͷ‫͕ࠩޡ‬հࡏ͠ͳ͍Α͏୅දՁ֨Λߟྀͤͣɼ௚఺Λ 1000 Ͱআ͓ͯ͘͠ɽʮ৽ंՁ֨ʯ͸ंछຖʹඪ४઀తʹ‫ंݸ‬ͷམࡳՁ֨Λ༧ଌ͢Δɽ͜ͷ༧ଌʹਂ૚ֶԽ͢Δɽ͞ΒʹʮφϏͷ༗ແʯʮϨβʔγʔτͷ༗ແʯशϞσϧΛద༻͢Δɽʮαϯϧʔϑͷ༗ແʯʹ͍ͭͯ͸৽ं࣌ͷඪ४૷උঢ়ଶਂ૚ֶशϞσϧͷߏ੒ͱͯ͠தؒ૚Λ 3 ૚ͱ͠ɼ֤ͱಉҰͰ͋Ε͹ 0ɼෆ଍͍ͯ͠Ε͹-1ɼ૿ઃ͞Ε͍ͯΕχϡʔϩϯ਺ΛԼҐ૚͔Βॱʹ j = 40, k = 8, l = 4
͹+1 ͱ͢Δɽ
ͱ͢Δɽͳ͓աֶशΛ཈͑Δ΂͘ɼதؒ૚ୈ 1 ૚ʹ͸༧ଌ݁ՌΛද 4 ͓Αͼਤ 6,7 ʹࣔ͢ɽημϯʹ͍ͭͯফ‫ ཰ڈ‬40% ͷυϩοϓΞ΢τΛద༻͢Δɽ‫׆‬ੑԽؔ਺͸ɼද 3 ͷϥϯμϜϑΥϨετΑΓߴ͍༧ଌਫ਼౓Λಘʹ͍ͭͯ͸ɼதؒ૚ୈ 1 ૚ͱୈ 3 ૚ʹਖ਼‫ن‬Խઢ‫਺ؔܗ‬
͍ͯΔɽ͜Ε͸ 3 ষͷ࣌‫ྻܥ‬༧ଌͱಉ༷ʹɼτϨϯυ(ҎԼɼReLU) Λ༻͍ɼதؒ૚ୈ 2 ૚ʹ߃౳ؔ਺Λ༻΍‫ق‬અੑมಈ͕ൺֱతখ͍͞΄Ͳ༧ଌ͠΍͘͢ɼ͞Β͍Δɽͳ͓ຊ‫ڀݚ‬͸ճ‫ؼ‬໰୊ (࿈ଓ஋ͷ༧ଌ) Ͱ͋Δͨʹ௚઀༧ଌʹΑΓ‫͕ࠩޡ‬հࡏ͢ΔཁҼΛ‫ݮ‬Βͨ͠ޮՌΊɼग़ྗ૚ͷ‫׆‬ੑԽؔ਺΋߃౳ؔ਺ͱ͢Δɽ݁߹‫౓ڧ‬ͩͱߟ͑ΒΕΔɽܾఆ܎਺ R2 ʹ͓͍ͯ΋ɼ9 ंछத 5ͷॳ‫ظ‬஋ʹ͍ͭͯ͸ɼReLU Λ༻͍Δ૚΁݁߹͢Δ৔ंछʹ͓͍ͯද 3 Λ্ճΔ݁ՌΛಘ͕ͨɼਖ਼౴཰Ͱ͸߹͸ Xavier Glorot ͷॳ‫ظ‬஋ [7] Λɼ߃౳ؔ਺Λ༻͍Δ9 ंछத 2 ंछ͔͠ද 3 ͔Β޲্Ͱ͖͍ͯͳ͍ɽ͜ͷ૚΁݁߹͢Δ৔߹͸ He ͷॳ‫ظ‬஋ [8] Λ༻͍Δɽཧ༝ͱͯ͠ɼਂ૚ֶशͷઆ໌ม਺ʹτϨϯυ΍‫ق‬અੑதؒ૚Λଟ૚ʹ͢Δ΄Ͳޯ഑ফࣦ໰୊͕‫͜ى‬ΔͨΊɼʹؔ͢Δ৘ใΛ༻͍ͳ͔ͬͨͨΊɼ͜ΕΒͷཁҼ͕‫ڧ‬ࣄલֶशͱͯ͠ੵ૚ΦʔτΤϯίʔμ [9] Λద༻͢Δɽ͍ंछʹ͓͍ͯ͸લষͷํ๏࿦ͷํ͕‫ػ‬ೳͨ͠ͱߟ֤͑ΦʔτΤϯίʔμʹ͓͍ͯ৘ใѹॖ͢Δ͜ͱͰɼઆΒΕΔɽͦ͜Ͱࠓ‫ޙ‬͸ɼτϨϯυʹ͍ͭͯ͸ RNN ΍໌ม਺ͷಛ௃நग़Λߦ͏ɽͦͷ‫ޙ‬ϑΝΠϯνϡʔχϯ
LSTM ͳͲ࣌ؒߏ଄ʹରԠͨ͠‫ػ‬ցֶशϞσϧΛಋೖάͱͯ͠ɼਂ૚ֶशϞσϧશମΛ‫఻ٯࠩޡ‬೻๏ʹΑͬ͠ɼ‫ق‬અੑʹ͍ͭͯ͸݄΍༵೔ͳͲΛμϛʔม਺Ͱ༩ͯ࠶ֶश͢Δɽͦͷࡍʹ͸ɼ‫ࢣڭ‬৴߸ʹର͢Δೋ৐࿨͑Δํ๏Λ‫ݕ‬౼͢Δɽ

経営課題にAIを!ビジネスインフォマティクス研究会 (第10回)JSAI Special Interest Group on Business Informatics (SIG-BI #10)6
ද 4: ਂ૚ֶशʹΑΔ‫ंݸ‬མࡳՁ֨ͷ༧ଌ݁ՌΧςΰϦंछਖ਼౴཰

ܾఆ܎਺ R2ημϯημϯίϯύΫτίϯύΫτϛχόϯ̨̨̪̫̪̫ܰࣗಈंܰࣗಈं̡̨ϓϦ΢εϑΟοτσϛΦηϨφύδΣϩϑΥϨελʔλϯτϫΰϯ̧0.42050.44920.22830.27620.31820.30930.35930.4086
0.3941

0.94200.9306
0.89150.93280.94220.93400.97070.93740.9400·ͱΊຊ‫Ͱڀݚ‬͸த‫ंݹ‬ങऔళͷཱ৔ʹཱͪɼ1ϲ݄‫ޙ‬ͷத‫ंݹ‬ͷམࡳՁ֨Λ༧ଌͨ͠ɽͦͷࡍʹंछຖͷ୅දՁ֨ (தԝ஋) Λഔհ͢Δํ๏ͱɼഔհͤͣʹ௚઀༧ଌ͢Δํ๏Λ‫ݕ‬౼ͨ͠ɽͦͷ݁ՌɼτϨϯυ΍‫ق‬અੑมಈ͕େ͖͍ंछʹ͸લऀ͕ྑ͘ɼখ͍͞ंछʹ͸‫ྑ͕ऀޙ‬͔ͬͨɽ͜ͷཧ༝ͱͯ͠ɼ‫ऀޙ‬ͷ௚઀༧ଌ (ਂ૚ֶश)͸‫ࠩޡ‬ͷൃੜϓϩηεΛ࠷খ‫͖Ͱʹݶ‬Δ͕ɼલऀ (࣌‫ྻܥ‬Ϟσϧ) ͷΑ͏ʹ࣌ؒߏ଄Λߟྀ͍ͯ͠ͳ͍ɽͦ͜Ͱࠓ‫ޙ‬ͷ՝୊ͱͯ͠ɼRNN ΍ LSTM ͰτϨϯυΛߟྀ͠ɼμϛʔม਺Ͱ‫ق‬અੑΛද‫͠ݱ‬ɼ͞Βʹ‫཰ࠩޡ‬Λ‫ػ‬ցֶशͷධՁ‫ج‬४ʹ͢Δ͜ͱͰ௿Ձ֨ଳʹ͓͚Δ༧ଌ‫ࠩޡ‬ͷվળΛ໨ࢦ͢ɽँࣙຊ‫ڀݚ‬͸‫ࣜג‬ձࣾϓϩτίʔϙϨʔγϣϯͱͷ‫ڞ‬ಉ‫͋Ͱڀݚ‬Δɽͳ͓ຊ‫ڀݚ‬ͷҰ෦͸จ෦ՊֶলՊֶ‫ڀݚ‬අ‫ج‬൫‫( ڀݚ‬C)(No.16K00320) ͷॿ੒ʹΑΓߦͬͨɽࢀߟจ‫ݙ‬[1] ΦʔτΦʔΫγϣϯͷ͝Ҋ಺ — Used car System Solutions: http://www.ussnet.co.jp/auction/ (ࢀর೔:2018/8/1)[2] DataLine ࠪ ఆ α ʔ Ϗ ε:https://www.protog.co.jp/product/corporation/dataline satei.html (ࢀর೔: 2018/9/1)[3] ాதউਓ: ‫ݱ‬୅࣌‫ྻܥ‬෼ੳ, ‫ؠ‬೾ॻళ, 2006.
[4] Trevor Hasite, et al.: ౷‫ܭ‬తֶशͷ‫ ૅج‬ʵσʔλϚΠχϯάɾਪ࿦ɾ༧ଌʵ, ‫ཱڞ‬ग़൛, 2014.

ਤ 6: ‫ंݸ‬མࡳՁ֨ͷਅ஋ͱ༧ଌ஋ͷ૬ؔਤ (ϓϦ΢εͷ৔߹)[5] Zhi-Hua Zhou: Ensemble Methods: Foundations andAlgorithms, Chapman and Hall/CRC, 2012.
[6] ੢֞௨, ू߹஌ͱ͸Կ͔, தެ৽ॻ, 2013.
[7] Xavier Glorot and Yoshua Bengio: “Understanding
the diﬃculty of training deep feedforward neural networks,” Proceedings of the Thirteenth International
Conference on Artificial Intelligence and Statistics,
pp.249–256, 2010.
[8] Kaiming He, et al.: “Delving Deep into Rectifiers:
Surpassing Human-Level Performance on ImageNetClassification,” Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV),
pp.1026–1034, 2015.

ਤ 7: ‫ंݸ‬མࡳՁ֨ͷਅ஋ͱ༧ଌ஋ͷ૬ؔਤ (ύδΣϩͷ৔߹)[9] Yoshua Bengio, Pascal Lamblin, Dan Popovici, andHugo Larochelle: “Greedy layer-wise training of deepnetworks,” Advances in Neural Information Processing Systems, vol.19, p.153, MITPress, 2007.
[10] Diederik P. Kingma and Jimmy Ba: “Adam: A
Method for Stochastic Optimization,” arXiv preprint
arXiv:1412.6980, 2015.