Authorship attribution using committee machines with k-nearest neighbors rated voting

Abstract

Authorship attribution, namely determination of the author of a text, may become an extraordinarily complex and sensitive job due to its relatively difficult feature extraction phase and highly nonlinear nature. This paper proposes a classification tool using committee machines consisting of multilayered perceptron neural networks (MLP) to identify the author of a text. Each expert is an individual MLP learning complex inputoutput relation composed of 14 lexical, stylometric attributes extracted from the corpus. The resulting mapping after training is used to identify the texts in German language written by two different authors. Unlike other committee based classification tools individual answers of the experts are combined with a novel voting method, k-nearest neighbors rated voting. The proposed method shows very promising results when benchmarked with simple majority voting technique.

Continuous user authentication using multi-modal biometrics

“…A further investigation was conducted in order to determine the optimal level of linguistic performance. As showed in previous studies, FF-MLP, K-Nearest Neighbour (K-NN) and Radial Basis Function (RBF) have shown the ability to deal efficiently with a high-dimensional and small size datasets [44][45][46]. Each of the classification algorithms performed numerous iterations, changing the various network parameters in order to optimise the performance of the classifier.…”