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Wireless sensor networks are becoming ubiquitous in their use in security, defense, monitoring and tracking applications. Intrusion detection is an important problem for wireless sensor networks in defense and security applications. Since intrusions are rare, they need to be handled efficiently. This involves: 1) continuous monitoring for threats and intrusions, 2) rapid detection, and possibly even classification and tracking, of intrusions, and 3) rapid decision making. Furthermore, sensor networks are burdened by limited battery power, which creates the need for energy-efficient classification models to address this issue. Our goal in this work is to build local classification models in clustered sensor networks to perform efficient detection of rare events, while also improving the lifetime of the network by reducing energy losses. We propose a correlationbased scheme to partition the features observed by the sensor nodes into disjoint mutually uncorrelated feature subsets. An ensemble of local classifiers are then trained on these subsets. We implement our model on a cluster-based sensor network architecture (LEACH). To reduce energy losses, we provide an energy efficient routing scheme designed for the above model. Our experimental results on real and synthetic data show that the proposed technique provides benefits both in terms of accuracy of detection and energy savings of the network.