Data Mining Practical Machine Learning Tools and Techniques

Social Network Analysis

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.

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Neural networks for classification: a survey

The multiple traveling salesman problem (mTSP) is a generalization of the well-known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Moreover, the characteristics of the mTSP seem more appropriate for real-life applications, and it is also possible to extend the problem to a wide variety of vehicle routing problems (VRPs) by incorporating some additional side constraints. Although there exists a wide body of the literature for the TSP and the VRP, the mTSP has not received the same amount of attention. The purpose of this survey is to review the problem and its practical applications, to highlight some formulations and to describe exact and heuristic solution procedures proposed for this problem.

The multiple traveling salesman problem : an overview of formulations and solution procedures

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

From the Publisher:
Cliff Ragsdale is an innovator of the spreadsheet teaching revolution and is highly regarded in the field of management science. This new edition of Spreadsheet Modeling and Decision Analysis provides instruction in the most commonly used management science techniques and shows how these tools can be implemented using the most current version of Microsoft Excel for Windows. This text also focuses on developing both algebraic and spreadsheet modeling skills.

Spreadsheet modeling and decision analysis

A new approach to solving the multiple traveling salesperson problem using genetic algorithms

Combining a neural network with a genetic algorithm for process parameter optimization

The objective of this article is to illustrate how managers in the hotel industry can analyze and improve their brands’market efficiency using data envelopment analysis (DEA). The authors evaluated...

The Competitive Market Efficiency of Hotel Brands: An Application of Data Envelopment Analysis:

Over the past three decades Evolutionary Algorithms have emerged as a powerful mechanism for finding solutions to large and complex problems. A promising new evolutionary algorithm known as Differential Evolution (DE) was recently introduced and has garnered significant attention in the research literature. This paper introduces a modification to DE that enhances its rate of convergence without compromising solution quality. DE was recently shown to outperform several well-known stochastic optimization methods on an extensive set of test problems. Our Modified Differential Evolution (MDE) algorithm utilizes selection pressure to develop offspring that are more fit to survive than those generated from purely random operators. We demonstrate that MDE requires less computational effort to locate global optimal solutions to well-known test problems in the continuous domain.

Cliff T. Ragsdale

Papers

Spreadsheet modeling and decision analysis

A new approach to solving the multiple traveling salesperson problem using genetic algorithms

Combining a neural network with a genetic algorithm for process parameter optimization

The Competitive Market Efficiency of Hotel Brands: An Application of Data Envelopment Analysis:

Modified differential evolution: a greedy random strategy for genetic recombination