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Extensible Multi-criteria Optimization Classifier for Prediction of Chinese Semantic Word-formation Patterns

机译：预测汉语语义构词模式的可扩展多准则优化分类器

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Data mining has become crucial in modern science and industry. Data mining problems raise interesting challenges for different research domains. For the supervised learning methods, owing to the class-overlapping, inconsistent, non- compatible, and contradictory problems in real world applications, the predictive performance of multi-criteria optimization classifier (MCOC) and other traditional data mining approaches will rapidly degenerate. In this paper we put forward an novel extensible MCOC (EMCOC) based on the dependent function: firstly the matter-element models of input data and test data are built, then the weighted dependent degree of data are computed according to the definition of the dependent function, and EMCOC based on the matter-element models is built for predicting the patterns of Chinese semantic word- formation. Our experimental results and comparison with MCOC show that our proposed approach can increase the separation of different patterns, the predictive performance of semantic pattern of a new compound word. In conclusion, we know that some extension-based methods can be used to effectively deal with such challenging problems in data mining.
机译：数据挖掘在现代科学和工业中已变得至关重要。数据挖掘问题对不同的研究领域提出了有趣的挑战。对于监督学习方法，由于现实应用中的类重叠，不一致，不兼容和矛盾的问题，多准则优化分类器（MCOC）和其他传统数据挖掘方法的预测性能将迅速退化。本文提出了一种基于依存函数的可扩展MCOC（EMCOC）：首先建立输入数据和测试数据的物元模型，然后根据依存关系的定义计算加权依存度功能，并建立了基于物元模型的EMCOC来预测中文语义词的形成方式。我们的实验结果和与MCOC的比较表明，我们提出的方法可以增加不同模式的分离，提高新复合词的语义模式的预测性能。总之，我们知道可以使用一些基于扩展的方法来有效处理数据挖掘中的此类难题。

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