An incremental piecewise linear classifier based on polyhedral conic separation
- Authors: Ozturk, Gurkan , Bagirov, Adil , Kasimbeyli, Refail
- Date: 2015
- Type: Text , Journal article
- Relation: Machine Learning Vol. 101, no. 1-3 (2015), p. 397-413
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: In this paper, a piecewise linear classifier based on polyhedral conic separation is developed. This classifier builds nonlinear boundaries between classes using polyhedral conic functions. Since the number of polyhedral conic functions separating classes is not known a priori, an incremental approach is proposed to build separating functions. These functions are found by minimizing an error function which is nonsmooth and nonconvex. A special procedure is proposed to generate starting points to minimize the error function and this procedure is based on the incremental approach. The discrete gradient method, which is a derivative-free method for nonsmooth optimization, is applied to minimize the error function starting from those points. The proposed classifier is applied to solve classification problems on 12 publicly available data sets and compared with some mainstream and piecewise linear classifiers. © 2014, The Author(s).
Optimization based clustering algorithms for authorship analysis of phishing emails
- Authors: Seifollahi, Sattar , Bagirov, Adil , Layton, Robert , Gondal, Iqbal
- Date: 2017
- Type: Text , Journal article
- Relation: Neural Processing Letters Vol. 46, no. 2 (2017), p. 411-425
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
- Reviewed:
- Description: Phishing has given attackers power to masquerade as legitimate users of organizations, such as banks, to scam money and private information from victims. Phishing is so widespread that combating the phishing attacks could overwhelm the victim organization. It is important to group the phishing attacks to formulate effective defence mechanism. In this paper, we use clustering methods to analyze and characterize phishing emails and perform their relative attribution. Emails are first tokenized to a bag-of-word space and, then, transformed to a numeric vector space using frequencies of words in documents. Wordnet vocabulary is used to take effects of similar words into account and to reduce sparsity. The word similarity measure is combined with the term frequencies to introduce a novel text transformation into numeric features. To improve the accuracy, we apply inverse document frequency weighting, which gives higher weights to features used by fewer authors. The k-means and recently introduced three optimization based algorithms: MS-MGKM, INCA and DCClust are applied for clustering purposes. The optimization based algorithms indicate the existence of well separated clusters in the phishing emails dataset. © 2017, Springer Science+Business Media New York.