A New Classification Based on Association Algorithm

Thabtah, Fadi; Mahmood, Qazafi; McCluskey, T.L.; Abdel-Jaber, Hussein

doi:10.1142/s0219649210002486

Cited by 25 publications

(16 citation statements)

References 13 publications

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“…42,43 The choice of these algorithms was based on their common use in practical applications and the different learning methods employed during the training process. 44,[45][46] .…”

Section: Specificity =mentioning

confidence: 99%

Ensemble Learning Model for Screening Autism in Children

Diabat¹,

Al-Shanableh²

2019

IJCSIT

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Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive, and social challenges. ASD screening is the process of detecting potential autistic traits in individuals using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large numbers of items to be covered by the user and they generate a score based on scoring functions designed by psychologists and behavioural scientists. Potential technologies that may improve the reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS performance has been measured on a real dataset related to cases and controls of children and using different Machine Learning techniques. The results revealed that ECAS was able to generate better classifiers from the children dataset than the other Machine Learning methods considered in regard to levels of sensitivity, specificity, and accuracy.

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“…42,43 The choice of these algorithms was based on their common use in practical applications and the different learning methods employed during the training process. 44,[45][46] .…”

Section: Specificity =mentioning

confidence: 99%

Ensemble Learning Model for Screening Autism in Children

Diabat¹,

Al-Shanableh²

2019

IJCSIT

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“…The most commonly cited datasets [18,19,21,26,37,38] are selected for the experiments. These all datasets are available at UCI Machine Learning Repository [36].…”

Section: Selection Of Datasets For Experimentsmentioning

confidence: 99%

Associative Classification using Automata with Structure based Merging

Abrar¹,

Sim²,

Abbas³

2019

IJACSA

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Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rules. The process used to generate association rules is exponential by nature; thus in AC, researchers focused on the reduction of redundant rules via rules pruning and rules ranking techniques. These techniques take an important part in improving the efficiency; however, pruning may negatively affect the accuracy by pruning interesting rules. Further, these techniques are time consuming in term of processing and also require domain specific knowledge to decide upon the selection of the best ranking and pruning strategy. In order to overcome these limitations, in this research, an automata based solution is proposed to improve the classifier's accuracy while replacing ranking and pruning. A new merging concept is introduced which used structure based similarity to merge the association rules. The merging not only help to reduce the classifier size but also minimize the loss of information by avoiding the pruning. The extensive experiments showed that the proposed algorithm is efficient than AC, Naive Bayesian, and Rule and Tree based classifiers in term of accuracy, space, and speed. The merging takes the advantages of the repetition in the rules set and keep the classifier as small as possible.

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“…They are often applied to tremendously large data sets. An example is the market basket data in grocery stores [39].…”

Section: A Machine Learning Approachmentioning

confidence: 99%

Sentiment Analysis or Opinion Mining: A Review

Saad¹,

Saberi²

2017

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.

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A New Classification Based on Association Algorithm

Cited by 25 publications

References 13 publications

Ensemble Learning Model for Screening Autism in Children

Ensemble Learning Model for Screening Autism in Children

Associative Classification using Automata with Structure based Merging

Sentiment Analysis or Opinion Mining: A Review

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