Harnessing the Power of Neural Networks for Classification of Microsoft Excel Data

This white paper explains the value of using NeuroXL Classifier neural network software for classification of Microsoft Excel data. The key benefits of neural network technology and the NeuroXL Classifier software are explained, and applications in finance, medicine, business, and research science are discussed. An example is given of using NeuroXL Classifier to categorize data which provides insight into the functionality and ease-of-use of the application.

The Value of Neural Networks for Classification
In finance, science, and business, analysts are often faced with the task of classifying items based on historical or measured data. Stock market analysts may wish to categorize a group of stocks as buy, sell, or hold; a cancer researcher may wish to categorize a list of tumors as benign or malignant; a mortgage analyst may wish to categorize loans as good or bad. A major difficulty faced by such analysts is that the data to be classified can often be quite complex, with numerous interrelated variables. The time and effort required to develop a model to solve accurately such classification problems can be significant.

Neural networks are a proven, widely used technology to solve such complex classification problems. Loosely modeled after the human brain, neural networks are interconnected networks of independent processors that, by changing their connections (known as training), learn the solution to a problem. NeuroXL Classifier software by AnalyzerXL implements self-organizing neural networks, which perform categorization by learning the trends and relationships within your data.

Despite their effectiveness, neural networks are often not used for classification due to their complexity and the learning required to implement them properly. NeuroXL Classifier removes these barriers by hiding the complexity of its advanced neural network-based methods while taking advantage of analysts' existing knowledge of Microsoft Excel spreadsheets. You simply supply the data, and NeuroXL Classifier implements a neural network that categorizes your data according to your preferences. Since users supply data through the familiar Excel interface, learning time is minimal, greatly reducing the interval between installing the software and performing classifications. The application is extremely intuitive and easy-to-use for beginners, not requiring any previous knowledge of neural networks, yet powerful enough for the most demanding professionals.

In summary, these are the key advantages of NeuroXL Classifier:

• Easy to learn and use
• No prior knowledge of neural networks required
• Integrates seamlessly with Microsoft Excel
• Provides proven neural network technology for highly accurate classification
• Detects relationships and trends in data that traditional methods overlook
• Lowest cost neural network classification product on the market

NeuroXL Classifier can be applied to problems in a variety of areas and industries, including:

Finance: NeuroXL Classifier's ability to handle numerous, often-interrelated variables makes it widely applicable to the financial industry. One such application is financial risk assessment, where NeuroXL Classifier can be used to categorize loan applications as good or bad. Another is stock market analysis, where a trader may wish to classify stocks as buy, hold, or sell based on historical data. Other applications include:

• Credit scoring.
• Bond ratings.
• Mortgage risk analysis.

Research Science: Researchers are often faced with the task of classifying chemicals, animals, cells, materials or other items based on measured or historical data. NeuroXL Classifier's ability to spot trends and relationships in large data sets makes it well suited for such applications. Specific examples include:

• Protein sequencing.
• Weather pattern classification.
• Air quality analysis.
• Insect gender determination.

Medicine: Neural networks have enjoyed widespread adoption in the field of medicine due to their ability to classify accurately diseases, genes, tumors, and other medical phenomena. Examples of the use of NeuroXL Classifier in this field include:

• Classification of EEG (electroencephalography) data of patients with sleep disorders.
• Tumor classification in breast cancer patients.
• Identification of genome types.

General Business: NeuroXL Classifier's advanced classification abilities and integration with Microsoft Excel make it a powerful and practical tool for solving business problems. Examples of typical applications include:

• Classification of sales prospects.
• Direct mail optimization.