Applied Decision Support with Soft Computing

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IDT publishes research on all aspects of intelligent decision technologies, from fundamental development to the applied system. The journal is concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems that have the potential to support decision making in the areas of management, international business, finance, accounting, marketing, healthcare, military applications, production, networks, traffic management, crisis response, human interfaces and other applied fields. The target audience is researchers in computer science, business, commerce, health science, management, engineering and information systems that develop and apply intelligent decision technologies.

Authors are invited to submit original unpublished work that is not under consideration for publication elsewhere. Editors-in-Chief Prof. George A. Associate Editors Prof. Ignac Lovrek, Ph. James F. Sharma canberra. Barry G. Hiro mgh. Jude Hemanth Karunya University India. Authors are requested to submit their manuscript electronically through the online submission system.

Contact one of the Editors-in-Chief if you need help. Gloria Phillips-Wren Email: gwren loyola. Jain Email: jainlakhmi gmail. Required files for final submissions After the article has been accepted, the authors should submit the final version as source files, including a word processor file of the text, such as Word or LateX If using LaTeX, please use the standard article.

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Authors whose native language is not English are advised to seek the advice of a native English speaker, before submitting their manuscripts. Peerwith offers a language and copyediting service to all scientists who want to publish their manuscript in scientific peer-reviewed periodicals and books. Manuscripts should be prepared with wide margins and double spacing throughout, including the abstract, footnotes and references. Every page of the manuscript, including the title page, references, tables, etc. However, in the text no reference should be made to page numbers; if necessary, one may refer to sections.

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Tables should not be included in the text. Column headings should be brief, but sufficiently explanatory. Standard abbreviations of units of measurement should be added between parentheses.

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Vertical lines should not be used to separate columns. Leave some extra space between the columns instead. The training examples that are closest to the maximum margin hyperplane are called support vectors. All other training examples are irrelevant for defining the binary class boundaries. SVM is simple enough to be analyzed mathematically since it can be shown to correspond to a linear method in a high dimensional feature space nonlinearly related to input space. In this sense, support vector machines may serve as a good candidate for combining the strengths of conventional statistical methods that are more theory-driven and easy to be analyzed and more data-driven, distribution free and robust machine learning methods [21].

The SVM approach has been introduced in several financial applications recently, mainly in the area of time series prediction and classification. Neural Networks Neural Networks were earlier thought to be unsuitable for data mining because of their inherent black-box nature. No information was available from them in symbolic form, suitable for verification or interpretation by humans. Recently there has been widespread activity aimed at redressing this situation, by extracting the embedded knowledge in trained networks in the form of symbolic rules.

This serves to identify the attributes that, either individually or in a combination, are the most significant determinants of the decision or classification. Unlike fuzzy sets, the main contribution of neural nets toward data mining stems from rule extraction and clustering [15]. Neural networks are especially applied to forecasting in business applications such as the forecasting of bankruptcy, stock price, index future and interest rates in finance and banking industries.

Genetic Algorithms Genetic Algorithm provides an algorithmic framework for exploiting heuristics that simulates natural - evaluation processes like selection and mutation. It evolves candidate solutions to problems that have large solution spaces and are not amenable to exhaustive search or traditional optimization techniques.

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  4. Genetic algorithms have been applied to a broad range of learning and optimization problems [22] since their inception by Holland [23]. Typically, a genetic algorithm starts with a random population of encoded candidate solution, called chromosomes. Through a recombination process and mutation operators, it evolves the population towards an optimal solution. Generating an optimal solution is not guaranteed and the challenge is thus to design a ge eti p o ess that maximizes the likelihood of generating such a solution.

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    The first step is typically to evaluate the fitness of each candidate solution in the current population, and to select the fittest candidate solutions to act as parents of the next generation of candidate solutions. After being selected for reproduction, parents are recombined using a crossover operator and mutated using a mutation operator to generate offsprings. The fittest parents and the new offsprings form a new population, from which the process is repeated to create new populations [24].

    Genetic algorithms are applied to different areas in business effectively. Especially scheduling, expenditure of allocated budget, customer reaction to promotions are the specific areas that genetic algorithms are applied during the decision making process.

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    Rule Induction Rule Induction RI creates a decision tree or a set of decision rules from training examples wi th a known classification [25]. The root node of a decision tree represents all examples in the training set.

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    If these examples belong to two or more classes, then the most discriminating attribute is selected and the set is split into multiple classes. This process of attribute selection and splitting is continued until each terminal node represents a different class of examples.

    The resulting decision tree is then applied to a test data set to evaluate its accuracy in classifying new examples. When a decision tree is overfitted to a training data set, its classification accuracy with new data may diminish. The tree must then be pruned to eliminate overfitting before it is deployed in a real life application. A decision tree may be translated into a set of rules.

    The classifyyication rules are normally stated in disjunctive normal form DNF. The model developed by RI is very attractive because it is easy to understand. A limitation of a decision tree-based model is that it creates only mutually exclusive classes; a RI algorithm can overcome this by creating rules for overlapping classes [7].

    Rule induction is applied to various areas in business such as risk classification, financial customer classification, market segmentation, fraud detection, product performance analysis and improvement of cross sales. Conclusion Parallel with the increase of data stored in databases of organizations, traditional statistical methods have become insufficient. Therefore, soft computing methods have been used in order to reveal the hidden and useful patterns stored in large databases.

    The soft computing methods, which are used in different areas of business, facilitate the decision making process and help managers. Each of these methods has different advantages according to the needs and aim of the organization. Also, in some cases the combination of these methods can be used in organizations as hybrid methods.

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    Especially in the areas such as finance, marketing, human resource management, telecommunication and banking, organizations prefer these methods in order to make prediction, classification, detection and association of data. By revealing the hidden and useful data, organizations can gain competitive advantage against their rivals in the market. References [1]. Neural Computation and Applications, Vol. Sookhanaphibarn, P.

    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing
    Applied Decision Support with Soft Computing Applied Decision Support with Soft Computing

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