Text Classification Using Modified Multi Class Association Rule

Kamaruddin, Siti Sakira; Yusof, Yuhanis; Husni, Husniza; Refai, Mohammad Hayel Al

doi:10.11113/jt.v78.9553

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…As reference [15] conducted a text classification using modified multi-class association rules, the proposed method was tested on a text classification problem, and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules. Meanwhile reference [16] compares the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Networks for emotion classification, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. Classification is very helpful in the process of learning on computers in computer knowledge working automatically [17] [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Syah

Al-Khowarizmi

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Digital image processing, in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides, classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using Mean Absolute Percent Error (MAPE) for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of Artificial Neural Network (ANN) is very suitable for optimizing classification using the Simple Evolving Connectionist System Method (SECoS), and as a result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.

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Section: Introductionmentioning

confidence: 99%

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Syah

Al-Khowarizmi

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…Unlike the traditional (single-task-based) ARM, the novel approach proposed in this paper is "multitaskbased ARM," which can discover more knowledge by jointly analyzing all tasks and by considering the relations between these tasks. The underlying assumption of our approach is that the rules of all tasks, or at least a association rules [13], multiclass association rules [14], multiobjective association rules [15], multimode association rules [16], multigranule association rules [17], multimodal semantic association rules [18], multilevel fuzzy association rules [19], and multiagent association rules [20]. In contrast to these present types, the new type proposed in this paper is a multitask association rule.…”

Section: Introductionmentioning

confidence: 99%

Multitask-based association rule mining

Taşer¹,

Birant²,

Birant³

2020

Turk J Elec Eng & Comp Sci

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Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then these local rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks. This work is licensed under a Creative Commons Attribution 4.0 International License. 933 YILDIRIM TAŞER et al./Turk J Elec Eng & Comp Scisubset of them, are familiar to one mutual rule set with a small difference. The proposed approach can discover common association rules by responding to different tasks and by applying an algorithm to consider all the tasks collectively. Accordingly, the rule X → Y can have high support and confidence in a task. A rule may not be frequent in the entire dataset; however, it may be frequent in several specific tasks. Therefore, finding task-based frequent rules in an effective way is important. This paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to discover rules by jointly considering multiple tasks. The proposed algorithm mines datasets that include task information. The MTARM approach consists of two phases. In the first phase, the algorithm discovers local frequent rules (single-task rules) from the data of each task separately, and in the second phase, it combines these local rules to produce the global result (multitask rules). The discovered multitask rules can be used to deploy information systems that support the execution of their associated task or to improve decision-making in applications.The main contributions and novelty of this study are threefold: (i) this is the first study that applies ARM to multitask problems; (ii) it proposes the MTARM approach, which attempts to find intertask rules based solely on the task identities and the observed data for each task; and, finally (iii) it also introduces two novel concepts: single-task rule and multitask rule. This paper also provides a brief survey of ARM as it is an active research area of data mining. In addition, it presents the results of experimental studies conducted on four different real-world datasets to demonstrate the capability of the proposed algorithm.The rest of the paper is organized...

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Text Classification Using Modified Multi Class Association Rule

Cited by 2 publications

References 17 publications

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Multitask-based association rule mining

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