In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, introduce splitting strategy iSAT, allows ADF records even if they are associated with previously unseen classes. is capable of identifying and handling concept drift; it, however, does not forget ga...

Recent years have witnessed growing interests in online incremental learning. However, there are three major challenges this area. The first difficulty is concept drift, that is, the probability distribution streaming data would change as arrives. second catastrophic forgetting, forgetting what we learned before when learning new knowledge. last one often ignore of latent representation. Only g...

At this current time, data stream classification plays a key role in big analytics due to its enormous growth. Most of the existing methods used ensemble learning, which is trustworthy but these are not effective face issues learning from imbalanced data, it also supposes that all pre-classified. Another weakness takes long evaluation time when target contains high number features. The main obj...

In this paper we present a general approach to learning in domains in which concepts change over time. We introduce a new metric, concept instability, that detects whether a concept is changing by examining changes in the concept representation formed by a supervised incremental learning algorithm. When a change is detected, we form the hypothesis that the change is due to a shift in the underl...

Over the past few years, a considerable number of studies have been made on Support Vector Machines (SVMs) in many domains to improve classification or prediction. However, SVMs request high computational time and memory when the datasets are large. Although incremental learning techniques are viewed as one possible solution developed to reduce the computation complexity of the scalability prob...

A fundamental problem when performing incremental learning is that the best set of a classification system’s parameters can change with the evolution of the data. Consequently, unless the system self-adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that perfo...

This paper presents an incremental learning algorithm within the framework of a fuzzy intelligent system. The incremental learning algorithm is based on priority values attached to fuzzy rules. The priority value of a fuzzy rule is generated based on the fuzzy belief values of the fuzzy rule derived from the training data. The fuzzy incremental algorithm has three important properties. It can d...

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically...

An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contri...