GA genetic algorithm HGA hybrid genetic algorithm SA simulated annealing algorithm ACO ant colony optimization TS tabu search IA immune algorithm GDA great deluge algorithm CEA cellular evolutionary approach NN neural network NFT near feasible threshold UGF universal generating function MSS multi-state system RB/i/j recovery block architecture that can tolerate i hardware and j software faults ...

Data replication is a technique for increasing the availability of data. Two popular algorithms for maintaining consistency among the replicas are Weighted Voting [1] and Available Copies [2] In recent papers [3] it has been shown that under common circumstances Available Copies (AC) performs better than Weighted Voting (WV). However, the issue of network partitioning due to gateway crashes is ...

Ensemble learning has attracted considerable attention owing to its good generalization performance. The main issues in constructing a powerful ensemble include training a set of diverse and accurate base classifiers, and effectively combining them. Ensemble margin, computed as the difference of the vote numbers received by the correct class and the another class received with the most votes, i...

Crowdsourcing has become an effective and popular tool for human-powered computation to label large datasets. Since the workers can be unreliable, it is common in crowdsourcing to assign multiple workers to one task, and to aggregate the labels in order to obtain results of high quality. In this paper, we provide finite-sample exponential bounds on the error rate (in probability and in expectat...

Consider an election between two candidates in which the voters’ choices are random and independent and the probability of a voter choosing the first candidate is p > 1/2. Condorcet’s Jury Theorem which he derived from the weak law of large numbers asserts that if the number of voters tends to infinity then the probability that the first candidate will be elected tends to one. The notion of inf...

In this paper, we use a bag-of-words of n-grams to capture a dictionary containing the most used ”words” which we will use as features. We then proceed to classify using four different classifiers and combine their results by apply a voting, a weighted voting and a classifier to obtain the real polarity of a phrase.

A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of la...

We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers ar...

In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmenta...

There are many methods of decision making by an ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers’ outputs. This work presents comparative analysis of some classifier fusion methods based on weighted voting of classifiers’ responses and combination of classifiers’ ...