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Soft computing techniques are collection of soft computing techniques methodology.

 • Exploit the tolerance for imperfection and uncertainty.

 • Provide capability to handle real-life ambiguous situations.

 • Try to achieve robustness against imperfection.

One of the most popular soft computing-based classification techniques is fuzzy classification. Fuzzy classes can better represent transitional areas than hard classification, as class membership is not binary but instead one location can belong to a few classes. In fuzzy set-based systems, membership values of data items range between 0 and 1, where 1 indicates full membership and 0 indicates no membership. Figure 2.3 shows a block diagram of fuzzy classification technique.

This section explains the various layers of analysis framework. Analytical framework is divided into user interface layer and processing layer. User interface layer is responsible for taking input from the user and processing. Processing layer is responsible for classification and comparison. Data access layer is responsible for connecting applications to databases for storing data. Figure 2.4 shows the system architecture and the interaction between the various components. Each layer is implemented use the class file that will implement the interface and data processing.

Figure 2.3 Fuzzy classification block diagram.

Figure 2.4 illustrates that the analytical framework consists of two layers where first layer provide user interface that allows users to select the desired dataset and algorithms and second layer provide processing component to selected algorithm.

Figure 2.4 Analysis framework architecture.