A simple KNN algorithm for text categorization

Soucy, Pascal; Mineau, Guy W.

doi:10.1109/icdm.2001.989592

Cited by 176 publications

(88 citation statements)

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“…The loan time information is 2 dimensions, respectively for user id and loan time [13]. The customer has a 2-dimensional record of the delinquent behavior, which is the user id and the overdue label.…”

Section: Data Preprocessingmentioning

confidence: 99%

Overdue Prediction of Bank Loans Based on Deep Neural Network

Li¹,

Sun²,

Li³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data growth.This paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm increase over 6% prediction than kNN, Bayes algorithm.

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Section: Data Preprocessingmentioning

confidence: 99%

Overdue Prediction of Bank Loans Based on Deep Neural Network

Li¹,

Sun²,

Li³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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“…To improve the retrieval accuracy of our system, k-nearest neighbor (k-NN), a widely used method in text classification [18] was employed. Text classification is the process of identifying the class to which a text document belongs.…”

Section: Prototype Iterationsmentioning

confidence: 99%

“…The k-nearest neighbor algorithm is simple and widely used in text classification [18]. An object is classified by a majority vote of its k-nearest neighbors.…”

Section: Identifying Confucius Entrymentioning

confidence: 99%

Confucius computer: a philosophical digital agent for intergenerational philosophical play

Cheok

Edirisinghe

Karunanayaka

2016

Pers Ubiquit Comput

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Confucianism is commonly defined as ''... a system of philosophical, ethical and political thought based on the teachings of Confucius,'' which originated through the teachings of Confucius during the sixth-century BCE. It is a way of life or a philosophy of human nature that considers human relationships as the foundation of the society. Confucius teachings had highly influenced the development of several cultures in Asia, making Confucianism an intangible cultural heritage. In this paper, we are re-acquainting users with an intangible heritage that is part of their everyday, by developing a system that permits experiencing Confucius teachings virtually and interactively. The system can measure philosophical intent of the human and generate meaningful philosophical answers. It is also aimed for intergenerational sharing of Confucius heritage through a simple interactive process with the virtual sage making the experience enjoyable and entertaining. Previous research in natural language processing (NLP) mainly focused on the understanding and delivering of human natural language accurately. In this research, we explored how to apply NLP to model the knowledge and teachings of Confucius, through the natural conversation between human and computer. This virtual Confucius, a chat agent that generates outputs based on Confucius teachings, using a series of algorithms and techniques to improve the matching accuracy between user input and computer output, introduces a novel way of interacting with intangible cultures. Our user evaluation results revealed that there is a positive correlation between relevance and enjoyment, finding their experiences interacting with virtual Confucius very encouraging. Adults who participated in experiencing the virtual Confucius together with their children believed that this system has the potential to improve intergenerational interactions through shared play.

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“…For the purposes of the experiments, we developed the kNN classification tool. For the SVM classifications, we used the Multi-Class Support Vector Machine of SVMlight from Cornell University and the University of Dortmund [2], [9]. We tested the classifications using the 'linear' kernel function for SVMlight, k being the number of classes for each dataset for kNN.…”

Section: Experiments and Evaluationsmentioning

confidence: 99%

Improved Gini-Index Algorithm to Correct Feature-Selection Bias in Text Classification

Park

Kwon

2011

IEICE Trans. Inf. & Syst.

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Heum PARK†a) , Nonmember and Hyuk-Chul KWON †b) , Member SUMMARY This paper presents an improved Gini-Index algorithm to correct feature-selection bias in text classification. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, an improved Gini-Index algorithm for feature selection, designed for text categorization and based on Gini-Index theory, was introduced, and it has proved to be better than the other methods. However, we found that the Gini-Index still shows a feature selection bias in text classification, specifically for unbalanced datasets having a huge number of features. The feature selection bias of the Gini-Index in feature selection is shown in three ways: 1) the Gini values of low-frequency features are low (on purity measure) overall, irrespective of the distribution of features among classes, 2) for high-frequency features, the Gini values are always relatively high and 3) for specific features belonging to large classes, the Gini values are relatively lower than those belonging to small classes. Therefore, to correct that bias and improve feature selection in text classification using Gini-Index, we propose an improved Gini-Index (I-GI) algorithm with three reformulated Gini-Index expressions. In the present study, we used global dimensionality reduction (DR) and local DR to measure the goodness of features in feature selections. In experimental results for the I-GI algorithm, we obtained unbiased feature values and eliminated many irrelevant general features while retaining many specific features. Furthermore, we could improve the overall classification performances when we used the local DR method. The total averages of the classification performance were increased by 19.4 %, 15.9 %, 3.3 %, 2.8 % and 2.9 % (kNN) in Micro-F1, 14 %, 9.8 %, 9.2 %, 3.5 % and 4.3 % (SVM) in Micro-F1, 20 %, 16.9 %, 2.8 %, 3.6 % and 3.1 % (kNN) in Macro-F1, 16.3 %, 14 %, 7.1 %, 4.4 %, 6.3 % (SVM) in Macro-F1, compared with tf*idf, χ 2 , Information Gain, Odds Ratio and the existing Gini-Index methods according to each classifier.

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A simple KNN algorithm for text categorization

Cited by 176 publications

References 0 publications

Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Network

Confucius computer: a philosophical digital agent for intergenerational philosophical play

Improved Gini-Index Algorithm to Correct Feature-Selection Bias in Text Classification

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