Improve Offensive Language Detection with Ensemble Classifiers
نویسندگان
چکیده
منابع مشابه
Offensive Language Detection Using Multi-level Classification
Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection wou...
متن کاملEnsemble Classifiers for Network Intrusion Detection System
Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (AN...
متن کاملEvaluation of Ensemble Classifiers for Intrusion Detection
One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are ana...
متن کاملAutomated Hate Speech Detection and the Problem of Offensive Language
A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced...
متن کاملRandom Projection Ensemble Classifiers
We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset’s geometrical structure to a given distortion. This reduc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Intelligent Systems and Applications in Engineering
سال: 2020
ISSN: 2147-6799
DOI: 10.18201/ijisae.2020261592