Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

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Abstract:Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

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Classification of imbalanced datasets is one of the widely explored challenges of the decade. The imbalance occurs in many real world datasets due to uneven distribution of data into classes, i.e. one class has more instances while others have a few that results in the biased performances of traditional classifiers towards the majority class with large number of instances and ignorance of other classes with less data. Many solutions have been proposed to deal with this issue in various crisp and fuzzy methods. This paper proposes a new hybrid fuzzy weighted nearest neighbor approach to find better overall classification performance for both minority and majority classes of imbalanced data. Benefits of neighbor weighted K nearest neighbor approach i.e. assignment of large weights to small classes and small weights to large classes are merged with fuzzy logic. Fuzzy classification helps in classifying objects more adequately as it determines that how much an object belongs to a class. Experimental results exhibit the improvements in classification of imbalanced data of different imbalance ratios in comparison with other methods.

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the 'existing algorithm modification solution' to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

In today's world, security has become a major issue in our lives, and in this era, one cannot trust the government for handling their lifetime savings. That's where the bitcoin comes to our lives. In this chapter, the authors try to understand one of the famous innovative payment methods (Bitcoin), how it is used and the data structure (Merkle tree) that is used in it. Also, they discuss one of the most recent attacks that involved the use of bitcoin (Wanacry). Further, they try to understand how this hack succeeded in stealing 10,800 euros that is 8,74,290 rs from the hospital with the help of bitcoin. They also discuss the various bitcoin companies now emerging with their own security measures against such hacks.

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