Fault localization (FL) is the most arduous and time-consuming task during software debugging. It is delineated in the literature that different FL method shows superior results under distinct scenarios. There is no single technique available which always outperforms than all other existing FL techniques for each type of fault. It has also been reported that different learning techniques can be combined using ensemble classifier to generate better predictive performance that was impossible to be obtained with any of the constituent learning algorithms separately. This has motivated us to use an ensemble classifier for effective fault localization. We focus on three different families of fault localization techniques, viz., neural-network based(NNBFL), mutation based(MBFL), and spectrum based(SBFL), to achieve this. In total, we have considered eleven representative techniques from these three families of FL methods. Proposed underlying model is intuitive and simple as it is based only on the test execution results and statement coverage data. Our proposed EBFL method classifies the statements into two different sets viz., Non-Suspicious and Suspicious. It helps to reduce the search space significantly. Our experimental analysis shows that our proposed EBFL technique requires, on an average, 58% of less code examination as compare of the other contemporary fault localization techniques, viz., Tarantula, DStar, CNN, DNN etc.