DucDung Nguyen

An efficient method for simplifying support vector machines

In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed vector. Through the analysis of relation between vectors in the input and feature spaces, we present the construction of new vectors that requires to find the unique maximum point of a one-variable function on the interval (0, 1), not to minimize a function of many variables with local minimums in former reduced set methods. Experimental results on real life datasets show that the proposed method is effective in reducing number of support vectors and preserving machines generalization performance.

A bottom-up method for simplifying support vector solutions

The high generalization ability of support vector machines (SVMs) has been shown in many practical applications, however, they are considerably slower in test phase than other learning approaches due to the possibly big number of support vectors comprised in their solution. In this letter, we describe a method to reduce such number of support vectors. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed one. Through the analysis of relation between vectors in input and feature spaces, we present the construction of the new vectors that requires to find the unique maximum point of a one-variable function on (0,1), not to minimize a function of many variables with local minima in previous reduced set methods. Experimental results on real life dataset show that the proposed method is effective in reducing number of support vectors and preserving machines generalization performance

Visualization Support for User-Centered Model Selection in Knowledge Discovery and Data Mining

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

Visualization support for a user-centered KDD process

Viewing knowledge discovery as a user-centered process that requires an effective collaboration between the user and the discovery system, our work aims to support an active role of the user in that process by developing synergistic visualization tools integrated in our discovery system D2MS. These tools provide an ability of visualizing the entire process of knowledge discovery in order to help the user with data preprocessing, selecting mining algorithms and parameters, evaluating and comparing discovered models, and taking control of the whole discover process. Our case-studies with two medical datasets on meningitis and stomach cancer show that, with visualization tools in D2MS, the user gains better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge.

Chance discovery and learning minority classes

Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases.

Data and Knowledge Visualization in Knowledge Discovery Process

The purpose of our work described in this paper is to develop and put a synergistic visualization of data and knowledge into the knowledge discovery process in order to support an active participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process.

A User-Centered Visual Approach to Data Mining

We present a human-centered approach to model selection in machine learning and data mining that emphasizes and facilitates the active participation of the user in the knowledge discovery process with quantitative and qualitative evaluation of patterns/models. The key idea of such a model selection is it would result from a combination of a quantitative evaluation of model characteristics and performance metrics with a qualitative evaluation of patterns/model by the user. We develop data mining methods integrated with visualization tools in the user-centered visual system D2MS (Data Mining with Model Selection). We finally present a case-study of D2MS in mining stomach cancer data.

Extracting Meningitis Knowledge by Integration of Rule Induction and Association Mining

The meningitis dataset has been used for extracting meningitis knowledge by learning and mining methods. This paper reports the result of extracting knowledge from this dataset by a novel learning method called LUPC that integrates separate-and-conquer rule induction with association rule mining. We first briefly introduce the basic ideas of LUPC then describe experiments, extracted knowledge and the result evaluation. The extracted knowledge is concerned with factors important for diagnosis (DIAG and DIAG2), for detection of bacteria or virus (CULT_FIND and CULTURE) and for predicting prognosis (C_COURSE and COURSE).