Verifiable Reinforcement Learning via Policy Extraction

While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning by training decision tree policies, which can represent complex policies (since they are nonparametric), yet can be efficiently verified using existing techniques (since they are highly structured). The challenge is that decision tree policies are difficult to train. We propose VIPER, an algorithm that combines ideas from model compression and imitation learning to learn decision tree policies guided by a DNN policy (called the oracle) and its Q-function, and show that it substantially outperforms two baselines. We use VIPER to (i) learn a provably robust decision tree policy for a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree policy for a toy game based on Pong that provably never loses, and (iii) learn a provably stable decision tree policy for cart-pole. In each case, the decision tree policy achieves performance equal to that of the original DNN policy

An application of decision trees method for fault diagnosis of induction motors

Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree method as an effective solution to their field problems. In this paper, an application of decision tree method to classify the faults of induction motors is proposed. The original data from experiment is dealt with feature calculation to get the useful information as attributes. These data are then assigned the classes which are based on our experience before becoming data inputs for decision tree. The total 9 classes are defined. An implementation of decision tree written in Matlab is used for these data

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M

On the parity complexity measures of Boolean functions

The parity decision tree model extends the decision tree model by allowing the computation of a parity function in one step. We prove that the deterministic parity decision tree complexity of any Boolean function is polynomially related to the non-deterministic complexity of the function or its complement. We also show that they are polynomially related to an analogue of the block sensitivity. We further study parity decision trees in their relations with an intermediate variant of the decision trees, as well as with communication complexity.Comment: submitted to TCS on 16-MAR-200

Penerapan Algoritma Decision Tree untuk Penilaian Agunan Pengajuan Kredit

Masih terdapat kemungkinan kesalahan penilaian agunan sebagai acuan nilai kredit, yang akan membuka peluang terjadinya NPL. Jadi diperlukan suatu cara penilaian (prediksi nilai) yang cukup proporsional, kredibel dan akurat. Prediksi yang tidak akurat menyebabkan perencanaan manajemen kredit yang tidak tepat. Prediksi nilai agunan telah menarik minat banyak peneliti karena nilai pentingnya baik di teoritis dan empiris. Model yang berbeda dapat memberikan keakuratan yang berbeda pula. Karena itu penelitian ini bertujuan menerapkan algoritma decision tree C.45 untuk penilaian agunan pengajuan kredit. Penelitian ini menggunakan data agunan pengajuan kredit di Kota Banjarmasin. Evaluasi kinerja algoritma menggunakan precision and recall dan AUC kemudian dibandingkan dan dianalisa hasilnya antara metode analisis lain (Naive Bayes, K-NN) dengan hasil prediksi dengan metode klasifikasi algoritma C4.5. Hasilnya, Decision Tree C4.5 dapat diterapkan dalam penilaian agunan kredit dengan akurasi 71% dan Nilai AUC di atas 0,6. Decision Tree C4.5 memprediksi lebih akurat dari pada k-NN, Naive Bayes dan Perhitungan bias

Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data

Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the data correspond to data stream is data stream mining. In this paper, we propose the feature selection with online decision tree. At first, we construct online type decision tree to regard credit card transaction data as data stream on data stream mining. At second, we select attributes thought to be important for detection of illegal use. We apply VFDT (Very Fast Decision Tree learner) algorithm to online type decision tree construction