In this blog we are going to talk about decision tree algorithm. Yeah, you read it right. It is a tree, or it looks like a tree (upside down tree) which helps to take decision. How come a tree helps us to take decision? So how do we take any decision?

Machine learning has successfully leveraged modern data and provided computational solutions to innumerable real-world problems, including physical and biomedical discoveries. Currently, estimators could handle both scenarios with all samples available and situations requiring continuous updates. However, there is still room for improvement on streaming algorithms based on batch decision trees and random forests, which are the leading methods in batch data tasks. In this paper, we explore the simplest partial fitting algorithm to extend batch trees and test our models: stream decision tree (SDT) and stream decision forest (SDF) on three classification tasks of varying complexities. For reference, both existing streaming trees (Hoeffding trees and Mondrian forests) and batch estimators are included in the experiments. In all three tasks, SDF consistently produces high accuracy, whereas existing estimators encounter space restraints and accuracy fluctuations. Thus, our streaming trees and forests show great potential for further improvements, which are good candidates for solving problems like distribution drift and transfer learning.

Decision Tree is a Supervised literacy manner that can be used for both group and Reversion cases, but mostly it's preferred for solving Set problems. It's a tree-structured classifier, where interior bumps represent the features of a dataset, branches character the decision rules and each slice bump represents the outcome. In a Decision tree, there are two nodes, which are the Decision Nodule and Leaf Node. Decision nodules are used to make any decision and have multiple branches, whereas Leaf nodules are the output of those judgments and don't contain any fresh branches. The diagnoses or the test are performed on the keystone of features of the given dataset.

Fumbling to find flashlights during blackouts may soon be a distant memory, as quantum computing and artificial intelligence could learn to decipher an electric grid's problematic quirks and solve system hiccups so fast, humans may not notice. Rather than energy grid faults turning into giant problems--such as voltage variations or widespread blackouts--blazing fast computation blended with artificial intelligence could rapidly diagnose trouble and find solutions in tiny splits of seconds, according to Cornell research forthcoming in Applied Energy (Dec. 1, 2021). "Energy power system failures are an old problem and we are still using classic computational methods to resolve them," said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in the College of Engineering. "Today's power systems can benefit from AI and the computational power of quantum computing, so power systems can be stable and reliable." You, along with doctoral student Akshay Ajagekar, are co-authors of "Quantum Computing-based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems."

Fumbling to find flashlights during blackouts may soon be a distant memory, as quantum computing and artificial intelligence could learn to decipher an electric grid's problematic quirks and solve system hiccups so fast, humans may not notice. Rather than energy grid faults turning into giant problems – such as voltage variations or widespread blackouts – blazing fast computation blended with artificial intelligence could rapidly diagnose trouble and find solutions in tiny splits of seconds, according to Cornell research forthcoming in Applied Energy (Dec. 1, 2021). "Energy power system failures are an old problem and we are still using classic computational methods to resolve them," said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in the College of Engineering. "Today's power systems can benefit from AI and the computational power of quantum computing, so power systems can be stable and reliable." You, along with doctoral student Akshay Ajagekar, are co-authors of "Quantum Computing-based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems."

Intelligent data-driven fault diagnosis methods have been widely applied, but most of these methods need a large number of high-quality labeled samples. It costs a lot of labor and time to label data in actual industrial processes, which challenges the application of intelligent fault diagnosis methods. To solve this problem, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method is proposed for the bearing fault diagnosis under the insufficient labeled samples situation. This method includes three stages: pre-training, deep clustering and enhanced supervised learning. In the first stage, a skip-connection based convolutional auto-encoder (SCCAE) is proposed and pre-trained to automatically learn low-dimensional representations. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) model that integrates the pre-trained auto-encoder with a clustering layer is proposed for deep clustering. Additionally, virtual adversarial training (VAT) is introduced as a regularization term to overcome the overfitting in the model's training. In the third stage, high-quality clustering results obtained in the second stage are assigned to unlabeled samples as pseudo labels. The labeled dataset is augmented by those pseudo-labeled samples and used to train a bearing fault discriminative model. The effectiveness of the method is evaluated on the Case Western Reserve University (CWRU) bearing dataset. The results show that the method can not only satisfy the semi-supervised learning under a small number of labeled samples, but also solve the problem of unsupervised learning, and has achieved better results than traditional diagnosis methods. This method provides a new research idea for fault diagnosis with limited labeled samples by effectively using unsupervised data.

The performance of a deep neural network (DNN) for fault diagnosis is very much dependent on the network architecture. Also, the diagnostic performance is reduced if the model trained on a laboratory case machine is used on a test dataset from an industrial machine running under variable operating conditions. Thus there are two challenges for the intelligent fault diagnosis of industrial machines: (i) selection of suitable DNN architecture and (ii) domain adaptation for the change in operating conditions. Therefore, we propose an evolutionary Net2Net transformation (EvoNet2Net) that finds the best suitable DNN architecture for the given dataset. Nondominated sorting genetic algorithm II has been used to optimize the depth and width of the DNN architecture. We have formulated a transfer learning-based fitness evaluation scheme for faster evolution. It uses the concept of domain adaptation for quick learning of the data pattern in the target domain. Also, we have introduced a hybrid crossover technique for optimization of the depth and width of the deep neural network encoded in a chromosome. We have used the Case Western Reserve University dataset and Paderborn university dataset to demonstrate the effectiveness of the proposed framework for the selection of the best suitable architecture capable of excellent diagnostic performance, classification accuracy almost up to 100\%.

Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data-driven solutions have received an immense attention in the industries systems for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to automatically update and retrain the diagnosers as the system or the environment change over time. Finally, unlike the model-based methods it is straight forward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model-based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We present the performance of our proposed approach through a comparative case study.

Power transformer is an essential component for the stable and reliable operation of electrical power grid. The traditional diagnostic methods based on dissolved gas analysis (DGA) have been used to identify the power transformer faults. However, the application of these methods is limited due to the low accuracy of fault identification. In this paper, a transformer fault diagnosis system is developed based on the combination of an improved genetic algorithm (IGA) and the XGBoost. In the transformer fault diagnosis system, the improved genetic algorithm is employed to pre-select the input features from the DGA data and optimize the XGBoost classifier. Performance measures such as average unfitness value, likelihood of evolution leap, and likelihood of optimality are used to validate the efficacy of the proposed improved genetic algorithm. The results of simulation experiments show that the improved genetic algorithm can get the optimal solution stably and reliably, and the proposed method improves the average accuracy of transformer fault diagnosis to 99.2\%. Compared to IEC ratios, dual triangle, support vector machine (SVM), and common vector approach (CVA), the diagnostic accuracy of the proposed method is improved by 30.2\%, 47.2\%, 11.2\%, and 3.6\%, respectively. The proposed method can be a potential solution to identify the transformer fault types.

In causal studies, systems of variables are described by causal models [18, 22], which are composed of two elements: (i) the graphical representation of relationships between variables in a model, encoded in a directed acyclic graph, and (ii) the mathematical description of conditional probability distribution of each variable given its causal parents. When a causal model involves hidden (i.e., unobserved) variables, any characterization of the model verifiable by observations should only include observed variables. Therefore, one of the objectives of causal inference is to eliminate all hidden variables from inequalities and equalities that describe the model. In principle, this can be achieved using the Tarski-Seidenberg quantifier elimination method [12]. However, its complexity is such that only models with few variables can be solved using this technique, hence the reason for the many attempts to simplify the problem.