Utilizing Imbalanced Data and Classification Cost Matrix to Predict Movie Preferences

Abstract

In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies' information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.

Tri-XGBoost Model: An Interpretable Semi-supervised Approach for Addressing Bankruptcy Prediction

“…Over the past decade, artificial intelligence techniques especially the machine learning methods such as case-based reasoning (CBR) (Rahayu and Suhartant, 2020), decision tree (DT) (Gepp et al 2010;Ocal et al 2015;Gepp and Kumar 2015), support vector machine (SVM) (Xie et al 2011;Erdogan 2013;Barboza et al 2017), artificial neural networks (ANN) (Jardin 2010; Callejón et al 2013) have been successfully used for predicting bankruptcies due to their ability to identify and represent a non-parametric and nonlinear relationship in the input dataset. However, these traditional machine-learning techniques do not work well with imbalanced datasets (Wang, 2018).…”

Classification Imbalanced Data Sets: A Survey

“…Usually, we deal with two terms which are called (positive class /minority class) and (negative class /majority class). this type of data creates a new problem in the field of data mining in all the cosmos areas [1][2][3][4]because the standard machine learning algorithms deal with unbalanced data without sensitivity to the unbalanced distribution of the classes which lead to a bias towards the (negative class/majority class). Sometimes this problem is not actually an issue for some applications because the norm classification algorithms depend on the balanced of the distributions of the class .but it is an actual issue for realworld applications like text classification [5],telecommunications, finances, oil spills detection using radar [2],e-mail foldering [6],the fraudulent calls detection [3],medical diagnosis [7],etcetera because as a result of during this standing, the extra interest of the learning is concentrated on the minority classes instead of the majority classes which wants to be correctly identified in these applications.…”