A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

doi:10.21275/v5i5.nov163675

Cited by 17 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature selection methods are usually used in classification tasks to reduce the dimensionality of large datasets [20]. Dimensionality reduction affects the performance of classification models since such models are trained on a subset of dataset features and thus save computational time.…”

Section: A Feature Selectionmentioning

confidence: 99%

Unmasking Fake Social Network Accounts with Explainable Intelligence

Alnagi,

Ahmad,

Al-Haija

et al. 2024

IJACSA

View full text Add to dashboard Cite

The recent global social network platforms have intertwined a web connecting people universally, encouraging unprecedented social interactions and information exchange. However, this digital connectivity has also spawned the growth of fake social media accounts used for mass spamming and targeted attacks on certain accounts or sites. In response, carefully constructed artificial intelligence (AI) models have been used across numerous digital domains as a defense against these dishonest accounts. However, clear articulation and validation are required to integrate these AI models into security and commerce. This study navigates this crucial turning point by using Explainable AI's SHAP technique to explain the results of an XGBoost model painstakingly trained on a pair of datasets collected from Instagram and Twitter. These outcomes are painstakingly inspected, assessed, and benchmarked against traditional feature selection techniques using SHAP. This analysis comes to a head in a demonstrative discourse demonstrating SHAP's suitability as a reliable explainable AI (XAI) for this crucial goal.

show abstract

Section: A Feature Selectionmentioning

confidence: 99%

Unmasking Fake Social Network Accounts with Explainable Intelligence

Alnagi,

Ahmad,

Al-Haija

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…The aim on feature reduction is to reduce the size of the numerical data to make it more interpretable (Widmann & Silipo, 2015). Broadly, feature reduction techniques achieve this by either removing or changing unnecessary features or creating a new, smaller set of features (Kumbhar & Mali, 2016). Model training and testing is where machine learning is applied to achieve the desired task (Guo, 2017).…”

Section: Extraction Of Meaning From Text Through Nlpmentioning

confidence: 99%

Applying natural language processing to automatically assess student conceptual understanding from textual responses

Somers

Cunningham-Nelson

Boles

2021

AJET

View full text Add to dashboard Cite

In this study, we applied natural language processing (NLP) techniques, within an educational environment, to evaluate their usefulness for automated assessment of students’ conceptual understanding from their short answer responses. Assessing understanding provides insight into and feedback on students’ conceptual understanding, which is often overlooked in automated grading. Students and educators benefit from automated formative assessment, especially in online education and large cohorts, by providing insights into conceptual understanding as and when required. We selected the ELECTRA-small, RoBERTa-base, XLNet-base and ALBERT-base-v2 NLP machine learning models to determine the free-text validity of students’ justification and the level of confidence in their responses. These two pieces of information provide key insights into students’ conceptual understanding and the nature of their understanding. We developed a free-text validity ensemble using high performance NLP models to assess the validity of students’ justification with accuracies ranging from 91.46% to 98.66%. In addition, we proposed a general, non-question-specific confidence-in-response model that can categorise a response as high or low confidence with accuracies ranging from 93.07% to 99.46%. With the strong performance of these models being applicable to small data sets, there is a great opportunity for educators to implement these techniques within their own classes. Implications for practice or policy: Students’ conceptual understanding can be accurately and automatically extracted from their short answer responses using NLP to assess the level and nature of their understanding. Educators and students can receive feedback on conceptual understanding as and when required through the automated assessment of conceptual understanding, without the overhead of traditional formative assessment. Educators can implement accurate automated assessment of conceptual understanding models with fewer than 100 student responses for their short response questions.

show abstract

“…Figure 1.2 gives a brief flowchart for the aforementioned three types of FTS methods. There is a significant number of researches that describe the critical role of filter FTS methods in analyzing high-dimensional classification problems such as text classification, sentiment classification, and disease detection based on its performance regarding time efficiency and learning accuracy [33][34] [35].…”

Section: Momentousness Of Feature Selection (Fts)mentioning

confidence: 99%

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

View full text Add to dashboard Cite

It is a meaningful and unforgettable journey toward my Doctoral degree and a critical time that helps me not only Enhance my research abilities but also gain a fresh perspective on life and rediscover myself."A thousand people, a thousand Hamlets." I believe it works for how we feel about this journey. For me, it is more like a war with myself, in which I would like to overcome resistance to change, be brave, and move on. There was a time when I mistook stability for safety and comfort, but after a while, I realized that stability comes from changes since the essence of life is change. Hence, I sincerely appreciate those who support me and encourage me to be brave and complete this vital journey. Without them, I cannot make any of these advancements.I want to first express my most heartfelt appreciation to Professor Nasser Fard, my advisor, for his unwavering support, guidance, and empathy during my graduate studies. He is always there to offer me support whenever I come up with new research ideas. my research would not have been possible to start, continue, or finish without his support. My foremost gratitude also goes to my committees, Prof. Chun-An Chou, Prof. Wei Xie, and Prof. Keivan Sadeghzadeh. I thank them for their wholehearted endorsement, including helpful suggestions that contributed greatly to this dissertation and valuable insights into my research.I also would like to thank my fellow Ph.D. colleagues in the MIE department for their support and encouragement throughout my graduate studies. I cherish all my memories with them, from the midnight cramming and refreshment breaks to uplifting affirmations and talks.Finally, I would like to thank my family, especially my parents. Their caring nature towards me never ceased despite the distance, and I still remember the warmth I felt every time I received an overseas call from them. They have always been my harbor and have made me brave enough to face all challenges I met in this journey. It is their unconditional backing that has formed the cornerstone of my life.

show abstract

A Survey on Feature Selection Techniques and Classification Algorithms for Efficient Text Classification

Cited by 17 publications

References 10 publications

Unmasking Fake Social Network Accounts with Explainable Intelligence

Unmasking Fake Social Network Accounts with Explainable Intelligence

Applying natural language processing to automatically assess student conceptual understanding from textual responses

Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness

Contact Info

Product

Resources

About