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Full-Text Articles in Databases and Information Systems
Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang
Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang
Research Collection School Of Computing and Information Systems
In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a 'data-centric approach' where the evaluation data is assumed to represent the ground truth'. The standard statistical approaches take evaluation and deviation at face value. We argue that attention should be paid to the subjectivity of evaluation, judging the evaluation score not just on 'what is being said' (deviation), but also on 'who says it' (reviewer) as well as on 'whom it is said about' (object). Furthermore, we observe that bias …
Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan
Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan
Research Collection School Of Computing and Information Systems
Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning …
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead …