Public lecture in Data Mining

Jean-Charles Lamirel helds public lecture "Feature selection: a key technique for data mining" on 30 October at 10am in J. Liivi 2 room 317.

Abstract:

Since the 1990s, advances in computing and storage capacity allow the manipulation of very large data. Whether in bioinformatics or in text mining, it is not uncommon that main mining algorithms like classifiers have to work with data description space of several thousands or even tens of thousands of features. One might think that such algorithms should be more efficient if there are a large number of features.

However, the first problem that arises is the increase in computation time. Moreover, the fact that a significant number of features are redundant or irrelevant to the classification task significantly perturbs their operation. As well as it is crucial in human learning, the integration of a feature selection process is also a main concern in the framework of the classification of high dimensional data.

For presenting the existing methods and their limitations in an highly multidimensional context, the following course will use a central example related to a complex and “real life” text mining task.

The first part of the presentation will introduce the main principle of classification and provides some examples of classifiers and of their application in the data mining domain. It will illustrate as well the effect of the manipulation of high dimensional data on the classifier results. Additional and usual problems related to the management of rare or imbalanced data and to highly similar classes will be also discussed in this part.

The second part of the presentation will focus on the description of the feature selection principle and on the one of the main categories of feature selection methods. Pros and cons of the usual methods will be discussed here and the effect of their application in combination with classifiers in the context of highly multidimensional data will be highlighted. The additional use of resampling techniques for the management of imbalanced data will also be investigated in this part.

The last part of the presentation will focus on one of our recent research in the domain of feature selection. The principle of this new promising approach based on the original theory of feature maximization and its associated metric will be explained. The behavior of the method will be compared to the ones of usual methods in the above mentioned context. Additional advantages related to specific class labeling and graph visualization capabilities and intrinsic properties of the method, like incrementality and non parametric behavior, will be finally discussed.