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   Pasricha, Ravdeep S.; Devineni, Pravallika; Papalexakis, Evangelos E.; Kannan, Ramakrishnan (January 2021, International Conference on Pattern Recognition (ICPR) 2020)

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2. Curbing Feature Coding: Strictly Local Feature Assignment

   Graf, Thomas (January 2020, Proceedings of the Society for Computation in Linguistics)

   Graf (2017) warns that every syntactic formalism faces a severe overgeneration problem because of the hidden power of subcategorization. Any constraint definable in monadic second-order logic can be compiled into the category system so that it is indirectly enforced as part of subcategorization. Not only does this kind of feature coding deprive syntactic proposals of their empirical bite, it also undermines computational efforts to limit syntactic formalisms via subregular complexity. This paper presents a subregular solution to feature coding. Instead of features being a cheap resource that comes for free, features must be assigned by a transduction. In particular, category features must be assigned by an input strictly local (ISL) tree-tot-tree transduction, defined here for the first time. The restriction to ISL transductions correctly rules out various deviant category systems. 

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3. Feature preserving data assimilation via feature alignment

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5. Feature Selections for Phishing URLs Detection Using Combination of Multiple Feature Selection Methods

   Abulfaz Hajizada and Sharmin Jahan (June 2023, ACM ICMLC 2023 : ACM--2023 15th International Conference on Machine Learning and Computing (ICMLC 2023))

   Cite: Abulfaz Hajizada and Sharmin Jahan. 2023. Feature Selections for Phishing URLs Detection Using Combination of Multiple Feature Selection Methods. In 2023 15th International Conference on Machine Learning and Computing (ICMLC 2023), February 17–20, 2023, Zhuhai, China. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3587716.3587790 ABSTRACT In this internet era, we are very prone to fall under phishing attacks where attackers apply social engineering to persuade and manipulate the user. The core attack target is to steal users’ sensitive information or install malicious software to get control over users’ devices. Attackers use different approaches to persuade the user. However, one of the common approaches is sending a phishing URL to the user that looks legitimate and difficult to distinguish. Machine learning is a prominent approach used for phishing URLs detection. There are already some established machine learning models available for this purpose. However, the model’s performance depends on the appropriate selection of features during model building. In this paper, we combine multiple filter methods for feature selections in a procedural way that allows us to reduce a large number of feature list into a reduced number of the feature list. Then we finally apply the wrapper method to select the features for building our phishing detection model. The result shows that combining multiple feature selection methods improves the model’s detection accuracy. Moreover, since we apply the backward feature selection method as our wrapper method on the data set with a reduced number of features, the computational time for backward feature selection gets faster. 

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