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Softopt has developed a data-mining package which uses a new, unpublished algorithm to analyze data and use it for making forecasts and predictions. It is a traditional tree-based classifier system, but the separations are found with hybrid genetic algorithms which result in high-quality separations. In comparison to neural networks our algorithm has the advantages of delivering forecasts in the form of probabilities with a statistical significance, of constructing forecast models much faster, of being able to describe characteristic subgroups and of avoiding data-overfitting. In comparison to traditional decision trees it has the advantages of discovering correlations between attributes much better, of avoiding the problem of data-overfitting and of delivering forecasts in the form of probabilities with a statistical significance.

Some example applications for the Softopt-data-mining package:
  • An insurance company has a large data-base of policy holders from the past. Each past policy holder can be judged to have been belonging to a certain risk-group. The insurance company wants to use personal information about a policy-holder (like age, income, marital status .....) to predict the probability that he belongs to the various risk-groups. The quality of such predictions is basically what defines the profit that a company makes.

  • In medicine it is often desirable to predict whether a patient has or does not have a disease from easily obtainable information about the patient like blood-pressure, sex, past illnesses .... However, one wants to keep the error-rate of misjudging an ill patient to be healthy very low, but one tolerates a larger error-rate of misjudging a healthy patient to be ill. There exists a large data-base of patients from the past containing the easily obtainable information and the information whether the patients had or did not have the disease.

  • Advertising agencies often have large records of past advertising campaigns and the reaction the campaign got from people. They want to use the information from the past to design a more specific campaign and approach the right kind of people.

SoftOpt offers consulting for data-mining problems. We offer to solve your data-mining problem, part of the solution can be a software tailored to your problem. The advantages for you are:
  • Fast solutions for your data-mining problems

    SoftOpt has many advanced and well tested data-mining algorithms implemented in software at its disposition. We can choose the best algorithm suitable for your specific problem.

  • There is no need for you to have an employee with knowledge and skills in data-mining

    Part of our solution for your problem is a software which is tailored specifically for your problem. The user interface is easy and data input and output can be done using comprehensible words. (The conversion and reconversion in and from numbers respectively takes place in the software.) Everybody can produce forecasts and analyses easily and confidently. With the present data-mining packages on the market you need an employee with data-mining skills who will spend weeks if not months with the modeling of your data-mining problem.

  • Confidentiality of your data is guaranteed by the way we collaborate with you

    If you have confidential data then we come to you, install the necessary software on one of your computers and work at your office. It will not be necessary to transfer data from your computer to one of ours at any time.

  • SoftOpt has developed some unique specialist data-mining algorithms for particular application domains

    For classification problems with many attributes (200 to 1000) SoftOpt has developed a brand new decision tree algorithm which uses genetic algorithms to find the separations and which decomposes each separation problem in a tree-like manner in subproblems with only very few attributes. This method is extremly fast considering the size of these problems and the quality of the separations is better than that of all the other conventional separation methods. For another type of classification problem where the task is to localize a small class SoftOpt has developed another very good algorithm which outperforms the conventional data-mining methods.

  • Objektive comparisons between different methods and algorithms are possible

    For the objective evaluation of predictions of probabilities SoftOpt has developed a method which is publicly available for free. This evaluation method is contained in the software which is part of a problem solution by SoftOpt and it enables you to objectively compare various data-mining methods with respect to the reliability of the forecasts.

  • Forecasts can be made very fast on every PC even for large data sets

    The data-mining process contains two parts:

    • Construction of a forecast model, the result being a model file.

    • New forecasts are produced using this model file.

    Most of the computing power is always needed for step 1. for the construction of the forecast model. Step 2. is always very fast. Forecasts are produced in a fraction of a second on any PC. Having millions of data of the past you can produce the model file on a powerful mainframe computer, then copy the model file on small PCs and laptops and produce the forecasts there.