Computational Intelligence and State-of-the-Art Data Analytics

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Now showing 1 - 4 of 4
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    Rapid Selection of Machine Learning Models Using Greedy Cross Validation
    ( 2022-01-04) Soper, Daniel
    This paper introduces a greedy method of performing k-fold cross validation and shows how the proposed greedy method can be used to rapidly identify optimal or near-optimal machine learning (ML) models. Although many methods have been proposed that apply metaheuristic and other search methods to the hyperparameter space as a means of accelerating ML model selection, the cross-validation process itself has been overlooked as a means of rapidly identifying optimal ML models. The current study remedies this oversight by describing a simple, greedy cross validation algorithm and demonstrating that even in its simplest form, the greedy cross validation method can vastly reduce the average time required to identify an optimal or near-optimal ML model within a large set of candidate models. This substantially reduced search time is shown to hold across a variety of different ML algorithms and real-world datasets.
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    Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores
    ( 2022-01-04) Khalifa, Muhammad ; Islam, Aminul
    Predicting the potential success of a book in advance is vital in many applications. This could help both publishers and readers in their decision-making process whether or not a book is worth publishing and reading, respectively. In this paper, we propose a model that leverages pretrained sentence embeddings along with various readability scores for book success prediction. Unlike previous methods, the proposed method requires no count-based, lexical, or syntactic features. Instead, we use a convolutional neural network over pretrained sentence embeddings and leverage different readability scores through a simple concatenation operation. Our proposed model outperforms strong baselines for this task by as large as 6.4\% F1-score points. Moreover, our experiments show that according to our model, only the first 1K sentences are good enough to predict the potential success of books.
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    An Algorithm for Generating Gap-Fill Multiple Choice Questions of an Expert System
    ( 2022-01-04) Sirithumgul, Pornpat ; Prasertsilp, Pimpaka ; Olfman, Lorne
    This research is aimed to propose an artificial intelligence algorithm comprising an ontology-based design, text mining, and natural language processing for automatically generating gap-fill multiple choice questions (MCQs). The simulation of this research demonstrated an application of the algorithm in generating gap-fill MCQs about software testing. The simulation results revealed that by using 103 online documents as inputs, the algorithm could automatically produce more than 16 thousand valid gap-fill MCQs covering a variety of topics in the software testing domain. Finally, in the discussion section of this paper we suggest how the proposed algorithm should be applied to produce gap-fill MCQs being collected in a question pool used by a knowledge expert system.
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