Towards a New Approach to Improve the Classification Accuracy of the Kohonen’s Self-Organizing Map During Learning Process

Abstract

Abstract-Kohonen self-organization algorithm, known as "topologic maps algorithm", has been largely used in many applications for classification. However, few theoretical studies have been proposed to improve and optimize the learning process of classification and clustering for dynamic and scalable systems taking into account the evolution of multi-parameter objects. Our objective in this paper is to provide a new approach to improve the accuracy and quality of the classification method based on the basic advantages of the Kohonen self-organization algorithm and on new network functions to pre-eliminate the auto-detected of drawbacks and redundancy.

Smart Model for Classification and Orientation of Learners in a MOOC

“…The unsupervised learning method is used to solve classification and clustering tasks. One of the neural network paradigms that uses this method is the SOM map, which has been used in several classification tasks and has undergone several improvements and evolutions to increase the relevance of the classification and the learning speed [16], [17]. (See Figure 3).…”

“…The input data is presented as a matrix, the rows are the vectors of the objects and the columns are the components of these objects. This paradigm uses competitive learning, in which it tries to distribute the training set into groups (clusters), which are specific to the input data [16], [17]. This type of neural network processes only the input vectors X and thus implements the "unsupervised" learning procedure.…”

“…The learning procedure starts with the normalization of the input data and the synaptic weights to an even length to reduce the learning time [16], [17]. This operation is based on the following algebraic formula:…”

“…The latter is the main core of the second block, which we have named "Intelligent block of classification and grouping of profiles". This block contains a preprocessing component that performs the normalization of training data and the elimination of correlation between data, especially the linear dependency between training objects [16], [17].…”

Beta hebbian learning: definition and analysis of a new family of learning rules for exploratory projection pursuit

“…For the first kind of data visualization usually techniques such as Exploratory Projection Pursuit (EPP) [15]- [18] can be used by projecting the data onto a low dimensional subspace in which we search for structures by visual inspection. For the second one, the topology preserving maps [19]- [23] may be applied, being probably the best known among these algorithms the Self-Organizing Map (SOM) [19], [21], [24], [25].…”