Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

The process of constructing new competencies in the family agricultural production unit

This article discusses the difficulties dairy farmers face when they decide to install a new type of production on their units. We intend to discuss the nature of the new competencies the farmers will construct in order to install new production ateliers, and to show the complexity of the means they used, the difficulties they face in this process, and the strategies farmers develop in consonance with the practical knowledge of their profession. The method used was Ergonomic Work Analysis, together with semi-structured interviews, done after sessions of observation and work analysis. The results show that it is possible to apprehend a part of the complexity of the process of constructing competencies among dairy farmers, the diversity of kinds of resources they mobilize, integrate and transfer in this construction process that materializes through their activities in the work context.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.