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1 c k Mendenhall 1887 Dickens, Thackeray, Mill,

2 Sebastiani 2002 k k nearest neighbor SVM Diederich et al., 2003; Teng et al., 2004 Breiman, RF; random forest ensemble learning Breiman 2001 classifier 1: N B 1,B 2,...,B i,..., B N 3 1 OOB Out-Of-Bag

3

4 : B i T i OOB OOB T i m try 3: B i OOB Breiman, 2001;, k k naive Bayes classifier k k k k-nn: k Nearest Neighbor Cover and Hart, 1967 k k k k k = ANN: Artificial Neural Network ANN HLNN: Hidden-layer Neural Network 1 Matthews and Merriam 1993 Kjell 1994 Tweedie et al Hoorn et al Waugh et al. 2000

5 259 LVQ Learning Vector Quantization Kohonen, 1985 LVQ LVQ LVQ LVQ1 OLVQ1 LVQ LVQ1 1: 2: 3: x R n m i R n c =argmin x m i i 4: m c m c(t +1)=m c(t)+α(t)[x(t) m c(t)] t α(t) α(t) x(t) m c(t), 2003 LVQ OLVQ1 LVQ SVM Support Vector Machine Vapnik 1995 (x 1,y 1), (x 2,y 2),...,(x m,y m) x =(x 1,x 2,...,x n) y 1 2 p f(x)= w ix i + b i=1 2 1 n f(x)= α iy ik(x i,x)+b i=1 K(x i,x) 2005 Diederich et al Joachims 1998

6 Teng et al Zheng et al SVM k-nn Bagging Bagging bootstrap aggregating Breiman 1996 bootstrap 1: m 2: 1 B B 3: B boosting Freund and Schapire, 1996 AdaBoost 1: w 1i 2: (t =1,2,...,T) a w ti b c d w (t+1)i = g(w ti) 3: T

7 N N-fold cross-validation N N N s (s =1,2,3,...,S 1) S 3 i (i =1,2,...,g) C i C i 4 recall precision R i P i : R i = ai a i + c i : P i = ai a i + b i macro average micro average C i,i=1,2,...,g : ˆR = 1 g g i=1 a i a i + c i : ˆP = 1 g g i=1 a i a i + b i F F F = 2 ˆP ˆR ˆP + ˆR 100 F 3.2 1, 1988; Jin and Murakami, 1993;, 1993;, 2000;, 1994, 1995, 1997, 2002, 2003, C i

8 , i j p ij P i =(p i1,p i2,...,p ij,...,p in) n j=1 pij = F s 20 s F 95 2 RF F 3 10 s 10 s F

9 s 10 s F 5

10 F 9 F F

11 F OOB

12 Breiman, L Bagging predictors, Machine Learning, 24, Breiman, L Random forests, Machine Learning, 45, Cover, T. M. and Hart, P. E Nearest Neighbor Pattern Classification, IEEE Transaction on Information Theory, IT-B Cristianini, N. and Shawe-Taylor, J Diederich, J., Kindermann, J., Leopold, E. and Paass, G Authorship attribution with support vector machines, Applied Intelligence, , Freund, Y. and Schapire, R. E Experiments with a new boosting algorithm, Proceedings of the Thirteenth International Conference on Machine Learning, , Morgan Kaufmann, San Fransisco. Hoorn, J. F., Frank, S. L., Kowalczyk, W. and Ham, F Neural network identification of poets using letter sequences, Literary and Linguistic Computing, 14 3, Jin, M. and Murakami, M Author s characteristic writing styles as seen through their use of commas, Behaviormetrika, 20 1, Joachims, T Text categorization with support vector machines, Proceedings of ICML-99, 16 th International Conference on Machine Learning Bled, SL, , , , n-gram 23 5, , ESTRELA, No.133, , Kjell, B Authorship determination using letter pair frequency features with neural network classifiers, Literary and Linguistic Computing, 9 2, Kohonen, T Self-Organizing Maps and Associative Memory, Springer Series in Information Science, 30, Springer-Verlag, Berlin, New York n-gram 22 6, version Matthews, R. A. J. and Merriam, T. V. N Neural computation in stylometry I: An application

13 267 to the works of Shakespeare and Fletcher, Literary and Linguistic Computing, 8 4, Mendenhall, T. C The characteristics curves of composition, Science, IX, Sebastiani, F Machine learning in automated text categorisation, ACM Computing Surveys, 34 1, Teng, G., Lai, M., Ma, J. and Li, Y authorship mining based on SVM for computer forensic, Machine Learning and Cybernetics, Proceedings of 2004 International Conference on, Vol Tweedie, F. J., Singh, S. and Holmes, D. I Neural network application in stylometry: The federalist papers, Computer and the Humanities, 30, Vapnic, V The Nature of Statistical Learning Theory, Springer, New York. Waugh, S., Adams, A. and Tweedie, F Computational stylistics using artificial neural networks, Literary and Linguistic Computing, 15 2, Zheng, R., Li, J., Chen, H. and Huang, Z A framework for authorship identification of online messages: Writing-style features and classification techniques, Journal of the American Society for Information Science and Technology, 57 3,

14 268 Proceedings of the Institute of Statistical Mathematics Vol. 55, No. 2, (2007) Authorship Identification Using Random Forests Mingzhe Jin and Masakatsu Murakami Faculty of Culture and Information, Doshisha University This paper proposes the use of Random Forests (RF) for authorship identification. It also reports a comparative study between RF and the following classifiers: k Nearest Neighbor, Support Vector Machines, Learning Vector Quantization, Bagging, and Boosting (AdaBoosting). We focused on the relationship between the performance of the classifiers in authorship identification and the size of training data. In this study, the following three different styles of text were used: 200 novels written by 10 great writers, 110 compositions written by 11 undergraduates, and 60 diaries written by 6 non-eminent writers. It is shown the that Random Forests algorithm is more effective and stable than the other classifiers. Key words: Authorship identification, text classification, stylometrics, Random Forests.

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