羟氨苄青霉素引起大疱性类天疱疮样疹

The nearest neighbor classifier is one of the most used and well-known techniques for performing recognition tasks. It has also demonstrated itself to be one of the most useful algorithms in data mining in spite of its simplicity. However, the nearest neighbor classifier suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and low noise tolerance. These weaknesses have been the subject of study for many researchers and many solutions have been proposed. Among them, one of the most promising solutions consists of reducing the data used for establishing a classification rule (training data) by means of selecting relevant prototypes. Many prototype selection methods exist in the literature and the research in this area is still advancing. Different properties could be observed in the definition of them, but no formal categorization has been established yet. This paper provides a survey of the prototype selection methods proposed in the literature from a theoretical and empirical point of view. Considering a theoretical point of view, we propose a taxonomy based on the main characteristics presented in prototype selection and we analyze their advantages and drawbacks. Empirically, we conduct an experimental study involving different sizes of data sets for measuring their performance in terms of accuracy, reduction capabilities, and runtime. The results obtained by all the methods studied have been verified by nonparametric statistical tests. Several remarks, guidelines, and recommendations are made for the use of prototype selection for nearest neighbor classification.

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Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Combination (ensembles) of classifiers is now a well established research line. It has been observed that the predictive accuracy of a combination of independent classifiers excels that of the single best classifier. While ensembles of classifiers have been mostly employed to achieve higher recognition accuracy, this paper focuses on the use of combinations of individual classifiers for handling several problems from the practice in the machine learning, pattern recognition and data mining domains. In particular, the study presented concentrates on managing the imbalanced training sample problem, scaling up some preprocessing algorithms and filtering the training set. Here, all these situations are examined mainly in connection with the nearest neighbour classifier. Experimental results show the potential of multiple classifier systems when applied to those situations.

New Applications of Ensembles of Classifiers

In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented.

Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System With Priority Customers

Introduction Representation Nearest Neighbour Based Classifiers Bayes Classifier Hidden Markov Models Decision Trees Support Vector Machines Combination of Classifiers Clustering Summary An Application: Handwritten Digit Recognition

Pattern Recognition: An Algorithmic Approach

https://sci2s.ugr.es/keel/pdf/specific/articulo/Devi02.pdf

An incremental prototype set building technique

A computer aided optimal method has been developed for the restoration of electric supply to areas isolated from the network following a fault in a distribution system. A search technique is used where the search is guided by appropriate heuristics. The optimum solution entails finding the strategy which involves the operation of minimum number of switchgear for rerouting the supply within the constraint of specified loading. This is an essential requirement in countries like India where the circuit breakers are almost always manually operated and a number of transformers and feeders operate close to their rated capacity. It pays therefore to adopt different strategies at peak load and off peak conditions since the number of breaker operations is so critical. The heuristic search that is developed is applied to a large distribution system and provides very good results. >

/pdf/optimal-restoration-of-power-supply-in-large-distribution-2tul1oygqh.pdf

Optimal restoration of power supply in large distribution systems in developing countries

Introduction Representation Nearest Neighbour Based Classifiers Bayes Classifier Decision Trees Support Vector Machines Combination of Classifiers Clustering Application: Handwritten Digit Recognition Summary and Conclusions.

Introduction To Pattern Recognition And Machine Learning

One of the simplest decision procedures that can be used for classification is the nearest neighbour (NN) rule. It classifies a sample based on the category of its nearest neighbour. When large samples are involved, it can be shown that this rule has a probability of error which is less than twice the optimum error—hence there is less than twice the probability of error compared to any other decision rule. The nearest neighbour based classifiers use some or all the patterns available in the training set to classify a test pattern. These classifiers essentially involve finding the similarity between the test pattern and every pattern in the training set.

V. Susheela Devi

Papers

Pattern Recognition: An Algorithmic Approach

An incremental prototype set building technique

Optimal restoration of power supply in large distribution systems in developing countries

Introduction To Pattern Recognition And Machine Learning

Nearest Neighbour Based Classifiers