An Overview of Inductive Learning Algorithms

AlMana, Amal M.; Aksoy, Mehmet Sabih

doi:10.5120/15340-3675

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…RULE Extraction System has been separated from the several covering algorithms due to its simplicity. RULES-1 were the first member of the RULES family [15]. Second version of the RULES has been developed and applied variousdomains [16].…”

Section: 2mentioning

confidence: 99%

Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

P*,

R B

2020

IJITEE

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Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.

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Section: 2mentioning

confidence: 99%

Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

P*,

R B

2020

IJITEE

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“…Some classification algorithms can be used with categorical and continuous predictors while others can be used with categorical only [41]. Also, some algorithms are more appropriate than others when it comes to the predicted field, or the target, as some are able to classify only categorical targets.…”

Section: Predictors Fields and Target Field(s) Typesmentioning

confidence: 99%

“…Handling missed data is a crucial subtask in data preprocessing phase [10,31,41,55]. That is one reason why the ratio of missing values is always importantly considered as a significant statistical metadata measure of the dataset and considered in many pieces of research [19,20,21,24,30,37].…”

Section: How Are Missing Data Handledmentioning

confidence: 99%

“…Symbolic algorithms are simple, easy to implement, interpret, and represent a good compromise between simplicity and complexity [40,43,46]. A lot of studies carried out to describe, review and compare these algorithms [10,31,41,42,45]. On one hand, it was reported that symbolic algorithms are a good choice for maximizing classification accuracy if the metadata of the dataset shows that the data has extreme distribution [10,45].…”

Section: A1 Symbolic Algorithmsmentioning

confidence: 99%

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Categorization of Factors Affecting Classification Algorithms Selection

2019

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A lot of classification algorithms are available in the area of data mining for solving the same kind of problem with a little guidance for recommending the most appropriate algorithm to use which gives best results for the dataset at hand. As a way of optimizing the chances of recommending the most appropriate classification algorithm for a dataset, this paper focuses on the different factors considered by data miners and researchers in different studies when selecting the classification algorithms that will yield desired knowledge for the dataset at hand. The paper divided the factors affecting classification algorithms recommendation into business and technical factors. The technical factors proposed are measurable and can be exploited by recommendation software tools.

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“…Handling these missed values is a very important subtask in data preprocessing phase [10,31,41,55]. That is one reason, why the ratio of missing values is always importantly considered as a significant statistical metadata measure of the dataset and considered in many pieces of research [19,20,21,24,30,37].…”

Section: How Are Missing Data Handledmentioning

confidence: 99%