Abstract
The paper provides a methodology for the integration of film information from different sources. To identify films information validation algorithms are used. The background and methodology are clearly described. This method can fuse the identical films and complete its information. In order to detect two related film information (they describe the identical film), we proposed three film information validation algorithms. All of these methods can detect films’ information which need integration processing. Our experiments show that method we proposed generally outperforms one-dimensional detection methods by different evaluation methods, i.e., precision, recall and F1.
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References
Ding, J., Liu, Y., Zhang, L., Wang, J., Liu, Y.: An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model. Appl. Intell. 44(2), 340–361 (2016)
Cerrada, M., Sánchez, R.V., Pacheco, F., Cabrera, D., Zurita, G., Li, C.: Hierarchical feature selection based on relative dependency for gear fault diagnosis. Appl. Intell. 44(3), 687–703 (2016)
Novoa-Hernández, P., Corona, C.C., Pelta, D.A.: Self-adaptation in dynamic environments - a survey and open issues. IJBIC 8(1), 1–13 (2016)
Adewumi, A.O., Arasomwan, M.A.: On the performance of particle swarm optimisation with(out) some control parameters for global optimisation. IJBIC 8(1), 14–32 (2016)
Wang, H., Wang, W., Sun, H., Rahnamayan, S.: Firefly algorithm with random attraction. IJBIC 8(1), 33–41 (2016)
Castelli, M., Vanneschi, L., Popovič, A.: Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. IJBIC 8(1), 42–50 (2016)
Rao, B.S., Vaisakh, K.: Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty. IJBIC 8(2), 67–83 (2016)
Cai, Q., Ma, L., Gong, M., Tian, D.: A survey on network community detection based on evolutionary computation. IJBIC 8(2), 84–98 (2016)
Rio de Souza e Silva Junior, L.D., Nedjah, N.: Distributed strategy for robots recruitment in swarm-based systems. IJBIC 8(2), 99–108 (2016)
Jia, Z., Duan, H., Shi, Y.: Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems. IJBIC 8(2), 109–121 (2016)
Srivastava, P.R.: Test case optimisation a nature inspired approach using bacteriologic algorithm. IJBIC 8(2), 122–131 (2016)
Xu, Z., Ünveren, A., Acan, A.: Probability collectives hybridised with differential evolution for global optimisation. IJBIC 8(3), 133–153 (2016)
Osuna-Enciso, V., Cuevas, E., Oliva, D., Sossa, H., Pérez-Cisneros, M.A.: A bio-inspired evolutionary algorithm: allostatic optimisation. IJBIC 8(3), 154–169 (2016)
Ahirwal, M.K., Kumar, A., Singh, G.K.: Study of ABC and PSO algorithms as optimised adaptive noise canceller for EEG/ERP. IJBIC 8(3), 170–183 (2016)
Niknam, T., Kavousi-Fard, A.: Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm. IJBIC 8(3), 184–194 (2016)
Alishvandi, H., Gouraki, G.H., Parvin, H.: An enhanced dynamic detection of possible invariants based on best permutation of test cases. Comput. Syst. Sci. Eng. 31(1), 53–61 (2016)
Parvin, H., Minaei-Bidgoli, B., Alinejad-Rokny, H.: A new imbalanced learning and dictions tree method for breast cancer diagnosis. J Bionanosci. 7(6), 673–678 (2013)
Parvin, H., Alinejad-Rokny, H., Minaei-Bidgoli, B., Parvin, S.: A new classifier ensemble methodology based on subspace learning. J. Exp. Theoret. Artif. Intell. 25(2), 227–250 (2013)
Parvin, H., Minaei-Bidgoli, B., Alinejad-Rokny, H., Punch, W.F.: Data weighing mechanisms for clustering ensembles. Comput. Electr. Eng. 39(5), 1433–1450 (2013)
Parvin, H., Alizadeh, H., Minaei-Bidgoli, B.: A new method for constructing classifier ensembles. JDCTA 3(2), 62–66 (2009)
Parvin, H., Alinejad-Rokny, H., Asadi, M.: An ensemble based approach for feature selection. J. Appl. Sci. Res. 7(9), 33–43 (2011)
Parvin, H., Alizadeh, H., Minaei-Bidgoli, B., Analoui, M.: CCHR: combination of classifiers using heuristic retraining. In: International Conference on Networked Computing and Advanced Information Management (NCM 2008) (2008)
Parvin, H., Alizadeh, H., Fathy, M., Minaei-Bidgoli, B.: Improved face detection using spatial histogram features. In: IPCV 2008, pp. 381–386 (2008)
Parvin, H., Alinejad-Rokny, H., Parvin, S.: A classifier ensemble of binary classifier ensembles. Int. J. Learn. Manag. Syst. 1(2), 37–47 (2013)
Parvin, H., Minaei-Bidgoli, B.: A clustering ensemble framework based on elite selection of weighted clusters. Adv. Data Anal. Classif. 7(2), 181–208 (2013)
Alizadeh, H., Minaei-Bidgoli, B., Parvin, H.: Optimizing fuzzy cluster ensemble in string representation. IJPRAI 27(2), 1350005 (2013)
Parvin, H., Beigi, A., Mozayani, N.: A clustering ensemble learning method based on the ant colony clustering algorithm. Int. J. Appl. Comput. Math. 11(2), 286–302 (2012)
Alizadeh, H., Minaei-Bidgoli, B., Parvin, H.: To improve the quality of cluster ensembles by selecting a subset of base clusters. J. Exp. Theoret. Artif. Intell. 26(1), 127–150 (2014)
Alizadeh, H., Minaei-Bidgoli, B., Parvin, H.: Cluster ensemble selection based on a new cluster stability measure. Intell. Data Anal. 18(3), 389–408 (2014)
Minaei-Bidgoli, B., Parvin, H., Alinejad-Rokny, H., Alizadeh, H., Punch, W.F.: Effects of resampling method and adaptation on clustering ensemble efficacy. Artif. Intell. Rev. 41(1), 27–48 (2014)
Parvin, H., Minaei-Bidgoli, B.: A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm. Pattern Anal. Appl. 18(1), 87–112 (2015)
Parvin, H., Mirnabibaboli, M., Alinejad-Rokny, H.: Proposing a classifier ensemble framework based on classifier selection and decision tree. Eng. Appl. AI 37, 34–42 (2015)
Parvin, H., Mohammadi, M., Rezaei, Z.: Face identification based on Gabor-wavelet features. Int. J. Digit. Content Technol. Appl. 6(1), 247–255 (2012)
Khan, M.A., Shahzad, W., Baig, A.R.: Protein classification via an ant-inspired association rules-based classifier. IJBIC 8(1), 51–65 (2016)
Lee, C.-P., Lin, W.-S.: Using the two-population genetic algorithm with distance-based k-nearest neighbour voting classifier for high-dimensional data. IJDMB 14(4), 315–331 (2016)
Zhu, M., Liu, S., Jiang, J.: A hybrid method for learning multi-dimensional Bayesian network classifiers based on an optimization model. Appl. Intell. 44(1), 123–148 (2016)
Kim, M.: Sparse inverse covariance learning of conditional Gaussian mixtures for multiple-output regression. Appl. Intell. 44(1), 17–29 (2016)
Tanveer, M., Shubham, K., Aldhaifallah, M., Nisar, K.S.: An efficient implicit regularized Lagrangian twin support vector regression. Appl. Intell. 44(4), 831–848 (2016)
Balasundaram, S., Meena, Y.: Training primal twin support vector regression via unconstrained convex minimization. Appl. Intell. 44(4), 931–955 (2016)
Yang, L., Qian, Y.: A sparse logistic regression framework by difference of convex functions programming. Appl. Intell. 45(2), 241–254 (2016)
Bang, S., Cho, H., Jhun, M.: Adaptive lasso penalised censored composite quantile regression. IJDMB 15(1), 22–46 (2016)
Chen, Y.-S., Cheng, C.-H., Chiu, C.-L., Huang, S.-T.: A study of ANFIS-based multi-factor time series models for forecasting stock index. Appl. Intell. 45(2), 277–292 (2016)
Su, W.: Key Technologies Research On Personalized WEB Business Information Integration System. Zhejiang University (2010)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
Dezert, J., Tchamova, A.: On the validity of Dempster’s integration rule and its interpretation as a generalization of bayesian integration rule. Int. J. Intell. Syst. 29(3), 223–252 (2014)
Tsanas, A., Zañartu, M., Little, M.A., et al.: Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information integration with adaptive Kalman filtering. J. Acoust. Soc. Am. 135(5), 2885–2901 (2014)
Khaleghi, B., Khamis, A., Karray, F.O., et al.: Multisensor data integration: a review of the state-of-the-art. Inf. Integr. 14(1), 28–44 (2013)
Jian, X., Jia, H., Shi, L.: Advances on multi-sensor information integration technologies. Chin. J. Constr. Mach. 7(2), 227–232 (2009)
Zu-ping, C.K.Z., Jun, L.: Multisource information integration: key issues, research progress and new trends. Comput. Sci. 8, 003 (2013)
Chunhui, S., Shengquan, M.A.: An information integration algorithm based on sugeno fuzzy complex-valued integral. J. Comput. Inf. Syst. 7(6), 2166–2171 (2011)
Hongguang, L., Xiuyan, S., Kaili, Z., Li, Z.: Study on multi-target tracking algorithm based on multi-source information integration using gray correlation analysis. J. Comput. Inf. Syst. 8(11), 4467–4474 (2012)
Keyhanipour, A.H., Moshiri, B., Kazemian, M., et al.: Aggregation of web search engines based on users’ preferences in WebIntegration. Knowl.-Based Syst. 20(4), 321–328 (2007)
Xie, N., Cao, C., Guo, H.Y.: A knowledge integration model for web information. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 67–72 IEEE, (2005)
Xu, Y., Xu, Z.M., Wang, X.L.: Multi-document automatic summarization technique based on information integration. Chin. J. Comput.-Chin. Ed. 30(11), 2048 (2007)
Dong, J., Zhuang, D., Huang, Y., et al.: Advances in multi-sensor data integration: algorithms and applications. Sensors 9(10), 7771–7784 (2009)
Chen, F., Steinbach, M., Kumar, V.: Introduction to Data Mining: Full Version, pp. 20–25. Posts & Telecom Press, Beijing (2011)
Song, L.: Research on Semantic Similarity Computation and Applications. Shandong University (2009)
Zhu, Y.X., Lu, L.Y.: Evaluation metrics for recommender systems. J. Univ. Electron. Sci. Technol. China 41(2), 163–175 (2012)
Hao, Z.: Clustering and Classification of Data and Text Using such Technologies as Genetic Algorithm. Tianjin University (2006)
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Dasturian, E., Parvin, H., Nejatian, S. (2017). Integrating Information of Films by a Multi-source Combining Framework. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_35
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DOI: https://doi.org/10.1007/978-3-319-62428-0_35
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