One metric used to measure the classification performance of K-Nearest Neighbors (K-NN) is F1-Score. K-NN is used here to classify data into shark behaviors, namely, Resting, Swimming, Feeding, and Non-Directed Motion (NDM). The objective of this paper is to improve the F1-Score of the K-NN. It is proven that applying Ensemble Averaging (EA) based filters on the data, prior to classification, improves Signal Power to Noise Power Ratio (SNR) and sequentially F1-Score. Data resizing and other Signal Processing (SP) techniques are also used to produce more accurate features.