International audienceUsing traditional Random Forests in short text classification revealed a performance degradation compared to using them for standard texts. Shortness, sparseness and lack of contextual information in short texts are the reasons of this degradation. Existing solutions to overcome these issues are mainly based on data enrichment. However, data enrichment can also introduce noise. We propose a new approach that combines data enrichment with the introduction of semantics in Random Forests. Each short text is enriched with data semantically similar to its words. These data come from an external source of knowledge distributed into topics thanks to the Latent Dirichlet Allocation model. Learning process in Random Forests is adapted to consider semantic relations between words while building the trees. Tests performed on search-snippets using the new method showed significant improvements in the classification. The accuracy has increased by 34% compared to traditional Random Forests and by 20% compared to MaxEnt
International audienceHuman action recognition is a challenging field that have been addressed with many different classification techniques such as SVM or Random Decision Forests and by considering many different kinds of information joints, key poses, joints rotation matrix, angles for example. This paper presents our approach for action recognition that considers only information given by the 3D joints from the skeleton and trains a two stage random forest to classify them. We extract skeletal features by computing all angles between any triplet of joints and all distances between any pair of joints then organizing them into a feature vector for each static pose. Complex dynamic actions are then described by sequences of such feature vectors. We evaluate our approach on the most recent and the largest benchmark, MSRC-12 Kinect Gesture Dataset, and compare our results with the state-of-the-art methods on this dataset
Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.
One of the main problems encountered when predicting student success, as a tool to aid students, is the lack of data used to model each student. This lack of data is due in part to the small number of students in each university course and also, the limited number of features that describe the educational background for each student. In this article, we introduce new features by augmenting the student feature space to obtain an improved model. These features are divided into several groups, namely, external added data, metric and counter data, and evolutive data. We will then assess the quality of the augmented data to classify at-risk students in their first year of university. For this article, the classifiers are built using Random Forests. As this learning method measures variable importance, we can enquire on the relevance of the augmented data, as well as the data groups that allow a more significant collection of features.
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