An Overview on Concept Drift Learning <s> II. CONCEPT DRIFT <s> Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners. <s> BIB001 </s> An Overview on Concept Drift Learning <s> II. CONCEPT DRIFT <s> The major problem of online learning or incremental learning is that, target function is frequently changing over time. This problem is commonly known as concept drift. Concept drift can be is further complicated if the dataset is class- imbalanced. There are different learning methods presented so far to handle concept drift like rule-based systems, decision trees, Naive Bayes, support vector machines, instance based learning, ensemble of classifiers, etc. This learning method requires further to combined with methods of drift detection in order to constantly monitor the performance of concept drift, however online changes detection was failed. In literature there are many methods presented for learning from data streams and drift detection, but most of methods failed to achieve speed and accuracy due to data inconsistency. In this project our goal is to present efficient method for online and non-parametric drift detection. This proposed method is based on recently presented Hoeffdings Bounds and HDDM. It handles concept drift regardless of the learning model to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. The existing system however as Naive Bayes classifier are having limitations, there is no scope to improve accuracy of HDDM. The Propose system will be efficiently provide drift detection method for data stream mining to improve accuracy. <s> BIB002
Concept drift happens when the target concept changes in a non-stationary environment. Let C1 and C2 be two target concepts, and I = {i 1 , i 2 , . . . , i n } an ordered instance sequence. Instances prior to i d have a stable concept C1 and does not change. After x instances, the concept stabilizes once more, but in another target concept C2. The concept among instances i d+1 and i d+ x is drifting between C1 and C2  . According to x length, the drift can be called gradual or abrupt. In gradual drift, the two concepts slowly swap; whereas in abrupt drift it sudden occurs. Wadewale and Desai BIB002 classify the variations of the target concept drift in sudden, incremental, gradual, recurring, blip and noise drifts. Fig. 1 contains this different types of drifts. Fig. 1a shows a sudden drift that the data changes instantly and without alternation. In incremental and gradual drifts the changes occur slowly. Incremental drift (Fig. 1b) happens when the data values gradually change over time, whereas gradual drift ( Fig. 1c ) also includes changing in class distribution. Recurring drifts (Fig. 1d ) occurs when instances of a concept temporary disappear and return after a while. Fig. 1e shows a rare event which in a static distribution can be considered as an outlier, and Fig. 1f shows random changes in instances (noise) that have to be filtered out BIB002 . Some authors BIB001 define the drifts in two types: real concept drift and virtual drift. Considering concept drift as changes in data distribution, the real concept drift occurs when the conditional distribution changes on the output whereas the input distribution remains unchanged. Virtual drift has different interpretations in the literature, as the changes in the distribution of incoming data, among others BIB001 .