Large scale multi-label text classification of a hierarchical dataset using Rocchio algorithm

Sowmya, B.; Chetan,; Srinivasa, K. G.

doi:10.1109/csitss.2016.7779373

Cited by 14 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Rocchio algorithm was first introduced by J.J. Rocchio [104] in 1971 as method of using relevance feedback to query full-text databases. Since then, many researchers have addressed and developed this technique for text and document classification [105,106]. This classification algorithm uses TF-IDF weights for each informative word instead of boolean features.…”

Section: Rocchio Classificationmentioning

confidence: 99%

Text Classification Algorithms: A Survey

et al. 2019

View full text Add to dashboard Cite

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.Spelling correction is an optional pre-processing step. Typos (short for typographical errors) are commonly present in texts and documents, especially in social media text data sets (e.g., Twitter). Many algorithms, techniques, and methods have addressed this problem in NLP [49]. Many techniques and methods are available for researchers including hashing-based and context-sensitive spelling correction techniques [50], as well as spelling correction using Trie and Damerau-Levenshtein distance bigram [51]. StemmingIn NLP, one word could appear in different forms (i.e., singular and plural noun form) while the semantic meaning of each form is the same [52]. One method for consolidating different forms of a word into the same feature space is stemming. Text stemming modifies words to obtain variant word forms using different linguistic processes such as affixation (addition of affixes) [53,54]. For example, the stem of the word "studying" is "study". LemmatizationLemmatization is a NLP process that replaces the suffix of a word with a different one or removes the suffix of a word completely to get the basic word form (lemma) [54][55][56]. Syntactic Word RepresentationMany researchers have worked on this text feature extraction technique to solve the loosing syntactic and semantic relation between words. Many researchers addressed novel techniques for solving this problem, but many of these techniques still have limitations. In [57], a model was introduced in which the usefulness of including syntactic and semantic knowledge in the text representation for the selection of sentences comes from technical genomic texts. The other solution for syntactic problem is using the n-gram technique for feature extraction. N-GramThe n-gram technique is a set of n-word which occurs "in that order" in a text set. This is not a representation of a text, but it could be used as a feature to represent a text.BOW is a representation of a text using its words (1-gram) which loses their order (syntactic). This model is very easy to obtain and the text can be represented through a vector, generally of a manageable size of the text. On the ...

show abstract

Section: Rocchio Classificationmentioning

confidence: 99%

Text Classification Algorithms: A Survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Sowmya et al [22] focused on solving the problem of automatically classifying large scale multi-label documents. They leveraged the Rocchio algorithm and the k-nearest neighbor (kNN) algorithm to implement a model that could categorize documents with hierarchical features.…”

Section: Research On Text Mining and Feature Learningmentioning

confidence: 99%

Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks

et al. 2021

View full text Add to dashboard Cite

Detecting and preventing targeted email attacks is a long-standing challenge in cybersecurity research and practice. A typical targeted email attack capitalizes on a sophisticated email message to persuade a victim to run a specific, seemingly innocuous, action such as opening a link or an attachment and downloading and installing a software program. To successfully perform such an attack without being noticed afterwards, the attached exploit documents (hereafter referred to as decoy documents), must contain content that is highly relevant to the target. An analysis of such decoy documents can provide crucial information for inferring and identifying the targeted or potentially harmed victims. In this paper, we propose an automatic approach that leverages natural language processing and machine learning to identify decoy documents that have a high chance of deceiving the targeted users. The experimental results show that the proposed method provides good prediction accuracy: the best result obtained on a collection of 200 Chinese decoy documents yielded an accuracy of 97.5%, an F-measure of 97.9% and a low FPR of 3.1%. The proposed scheme can be deployed at various access points to fortify the defense against targeted email attacks that threaten various targets.INDEX TERMS Targeted email attack, decoy document, machine learning, natural language processing

show abstract

“…Logistic regression [8] belongs to the classic statistical methods [4]. Other publications utilize the Rocchio algorithm that is compared against kNN in [22]. In another work [21], the authors design a pipeline involving the Rocchio algorithm.…”

Section: Related Workmentioning

confidence: 99%

LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision

Wittkopp,

Wiesner,

Scheinert

et al. 2021

Preprint

View full text Add to dashboard Cite

With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastructures. However, many methods based on artificial intelligence, such as supervised deep learning models, require large amounts of labeled training data to perform well. In practice, this data is rarely available because labeling log data is expensive, time-consuming, and requires a deep understanding of the underlying system. We present LogLAB, a novel modeling approach for automated labeling of log messages without requiring manual work by experts. Our method relies on estimated failure time windows provided by monitoring systems to produce precise labeled datasets in retrospect. It is based on the attention mechanism and uses a custom objective function for weak supervision deep learning techniques that accounts for imbalanced data. Our evaluation shows that LogLAB consistently outperforms nine benchmark approaches across three different datasets and maintains an F1-score of more than 0.98 even at large failure time windows.

show abstract

Large scale multi-label text classification of a hierarchical dataset using Rocchio algorithm

Cited by 14 publications

References 8 publications

Text Classification Algorithms: A Survey

Text Classification Algorithms: A Survey

Leveraging Machine Learning Techniques to Identify Deceptive Decoy Documents Associated With Targeted Email Attacks

LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision

Contact Info

Product

Resources

About