A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Abstract

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

Mitigating Academic Institution Dropout Rates with Predictive Analytics Algorithms

“…Source: (Lee & Chung, 2019) The review of available literature designates the varying efficiency of data mining classifiers in terms of accuracy, classification, and data sensitivity (See Figure 4). NB classifier demonstrated better precision and classification results than the other two (Ba'abbad et al, 2021;Ababneh, 2019). It recorded approximately 4% higher than 3-NN, DT, and NN.…”

“…It recorded approximately 4% higher than 3-NN, DT, and NN. The remaining showed varied efficiency (Ba'abbad et al, 2021). However, they all recorded a minimum accuracy rate of 81%, which indicates that PAAs can be the best approach to predicting school drop-outs (Ababneh, 2019).…”

“…They were used to illustrate how effective they are in predicting analytics in most academic institutions in North America. In a study to determine the most effective approach among the four models in analyzing the common drop-out risks, NB was considered the most effective due to its ability to analyze complex data (Ba'abbad et al, 2021;Ababneh, 2019). It was done using the Receiver Operating Characteristics (ROC) to evaluate the effectiveness of the models and the test data (Lee & Chung, 2019).…”

Algoritma Klasifikasi Data Mining Untuk Memprediksi Masyarakat Dalam Menerima Bantuan Sosial

“…Data pelatihan adalah data yang digunakan oleh algoritma data mining untuk mempelajari metrik klasifikasi untuk mengklasifikasikan data lain yaitu data pengujian. Dua set data artikel teks digunakan dan diklasifikasikan menjadi data pelatihan dan data pengujian [6].…”

Speech emotion classification using attention based network and regularized feature selection

“…Thereafter, the concatenation of these feature in vector form will be fed into a machine learning algorithm also referred to as classifiers in this context, for actual classification of emotion. Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Hidden Makov Model (HMM) and K-Nearest Neighbour (KNN) are popular classifiers 6 – 8 . Figure 1 shows a classical structure of emotion recognition.…”