Constructing Simpler Decision Trees from Ensemble Models Using Fourier Analysis
نویسندگان
چکیده
منابع مشابه
Distributed Data Mining for Pervasive and Privacy-Sensitive Applications
This paper considers the distributed data mining (DDM) problem where transmission or sharing of data is not desirable because of limited bandwidth or privacy-sensitive nature of the distributed, possibly multi-party, data. It notes that most DDM algorithms for such applications produce an ensemble of models (e.g. clusters, classifiers, and associations) generated from that data observed at diff...
متن کاملParallel tree-ensemble algorithms for GPUs using CUDA
We present two new parallel implementations of the tree-ensemble algorithms Random Forest (RF) and Extremely randomized trees (ERT) for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). Random Forest and Extremely randomized trees are ensemble learners for classification and regression. They operate by constructing a multitude of dec...
متن کاملMediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine
Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current de...
متن کاملImproving reservoir rock classification in heterogeneous carbonates using boosting and bagging strategies: A case study of early Triassic carbonates of coastal Fars, south Iran
An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...
متن کاملEnhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
Credit risk assessment is a critical topic for finance activity and bankruptcy prediction that has been broadly explored using statisticalmodels andMachine Learningmethods. Recently, studies have suggested the use of ensemble strategies to enhance credit modelling performance. However, accuracy is obtained at the expense of interpretability, leading to the reluctance of financial industry to em...
متن کامل