Broadband dielectric property measurements of biological tissues is mostly performed with open ended coaxial probe technique due to a number of advantages such as flexible sample shape and size. However, the technique is known to suffer from high error rates; thus, envisioned applications of the technique remains hampered by this problem. One way to mitigate such error for medical applications is to perform tissue classification with machine learning algorithms. In this work, Cole-Cole parameters of rat liver dielectric properties are used for training and testing of an in house Support Vector Machine (SVM) algorithm to enable malignant tissue classification. Cole-Cole parameters are fitted with Particle Swarm Optimization (PSO) to a total of 700 dielectric property measurements collected from 22 rats. The Cole-Cole parameters are fed to the SVM algorithm and k-fold cross validation is used to prevent the algorithm from memorizing the data. Hepatic malignancies are classified with 96% accuracy where a better accuracy is obtained in comparison to plain dielectric property measurement and also an automated decision making mechanism is enabled.