Continual learning from stationary and non-stationary data

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims

Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets