Kun Guo

Rough set and Tabu search based feature selection for credit scoring

Abstract As the credit industry has been growing rapidly, huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach, called FSRT, to feature selection based on rough set and tabu search is proposed. In FSRT, conditional entropy is regarded as the heuristic to search the optimal solutions. The proposed method is introduced to credit scoring and Japan credit dataset in UCI database is selected to demonstrate the competitive performance of the proposed method. Moreover, FSRT shows a superior performance in saving the computational costs and improving classification accuracy.

Cluster analysis on city real estate market of China: based on a new integrated method for time series clustering

Abstract After the reform of urban housing system in 1998, China real estate market had a rapid growth in recent years, while house price was increasing sharply. Using the House Price Indices of 70 cities in China from CREIS (China Real Estate Index System), we found that the house price of each city had an upward tendency with some certain stages. However, different cities also had their distinctive features. In this paper, a new integrated method for time series clustering is employed to do cluster analysis on city real estate market of China. The time series are firstly divided into several stages mainly based on the changes in government policy using wavelet analysis with expert experience. Then the variables that describe the character of each stage such as average growth rate and volatility are used as attributes of each city. Consequently, DBScan algorism for normal clustering can be used and the results show that there are several categories of growth modes of city real estate markets while the macro-control policies had different effect on each category.

DWWP: Domain-specific new words detection and word propagation system for sentiment analysis in the tourism domain

Abstract Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection (DW) and word propagation (WP) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on Dataset I and Dataset II, respectively.

Improved New Word Detection Method Used in Tourism Field

Abstract Chinese segmentation has attracted amounts of attention in natural language processing in recent years and is the basis of web text mining. The article improved statistics-based method EMI, then we use improved approach to detect new words in tourism field. The result demonstrates that our method can detect new words significantly, especially in detecting proper nouns and sentiment words which will be helpful in subsequent tasks such as sentiment analysis and word embedding. In additional, this paper analyze parameters which are influential on the effects of new words detection. At last, the article discussed possible application of new word detection in sentiment analysis.

Enhanced word embedding with multiple prototypes

Word representation is one of the basic word repressentation methods in natural language processing, which mapped a word into a dense real-valued vector space based on a hypothesis: words with similar context have similar meanings. Models like NNLM, C&W, CBOW, Skip-gram have been designed for word embeddings learning, and get widely used in many NLP tasks. However, these models assume that one word had only one semantics meaning which is contrary to the real language rules. In this paper we pro-pose a new word unit with multiple meanings and an algorithm to distinguish them by its context. This new unit can be embedded in most language models and get series of efficient representations by learning variable embeddings. We evaluate a new model MCBOW that integrate CBOW with our word unit on word similarity evaluation task and some downstream experiments, the result indicated our new model can learn different meanings of a word and get a better result on some other tasks.

Property Prices and Bank Lending: Some Evidence from China's Regional Financial Centres

Abstract By exemplifying the cases of Chinas twenty financial cities, this paper tries to identify the linkages between property prices and bank lending in Chinas regional financial centers and finds that long-run causality appears between property prices and bank lending for each financial center. Time series techniques and dynamic panel data model are used in this paper. Through analysis, this paper gives the conclusion that bank lending plays an important role in pushing up property prices. Property price indeed follows a dynamic process and the property prices of Beijing, Shanghai and Shenzhen change in a more stable way. In detail, people of the three financial centers in China Beijing, Shanghai and Shenzhen pay more attention to bank lending when buying their houses.

Word Similarity Fails in Multiple Sense Word Embedding

Word representation is one foundational research in natural language processing which full of challenges compared to other fields such as image and speech processing. It embeds words to a dense low-dimensional vector space and is able to learn syntax and semantics at the same time. But this representation only get one single vector for a word no matter it is polysemy or not. In order to solve this problem, sense information are added in the multiple sense language models to learn alternative vectors for each single word. However, as the most popular measuring method in single sense language models, word similarity did not get the same performance in multiple situation, because word similarity based on cosine distance doesn’t match annotated similarity scores. In this paper, we analyzed similarity algorithms and found there is obvious gap between cosine distance and benchmark datasets, because the negative internal in cosine space does not correspond to manual scores space and cosine similarity did not cover semantic relatedness contained in datasets. Based on this, we proposed a new similarity methods based on mean square error and the experiments showed that our new evaluation algorithm provided a better method for word vector similarity evaluation.

The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

Abstract Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

An Analysis Oncrude Oil Price Mutation in View of Zeeman's Catastrophe Machine

Abstract With the acceleration of internationalmarket integration and the frequent outbreak of international political and economic events, the volatility of oil priceshas continued toincrease in recent years. As the main source of energy, crude oil plays an important role in the development of a countrys economy. Therefore, it is meaningful to study the mutation of oil prices. Based on the Zeemans catastrophe machine, USDX and excess demand are selected as two main factors to construct the catastrophe model, which helps to explain the structural relationship between USDX and excess demand when the crude oil price mutates.

The Study of the Development of Chinese Stock Market Based on Factor Analysis

Abstract The article chooses nine indicators from scale, liquidity, financing function, investment function and effectiveness of stock market, using factor analysis to simplified them to three comprehensive indicators: capital allocation, investment and financing level and operation, then focuses on the different weight of each index based on its influence degree to build the index which can fully reflect the development of Chinese stock market. The results show that although the overall development of Chinese stock market in 2003-2012 was on the rise, there are still many problems, such as inefficient operation, the prevailing wind of speculation and the irrational investment behaviour.