Improved Random Forest For Feature Selection In Writer Identification

Writer Identification (WI) is a process to determine the writer of a given handwriting sample. A handwriting sample consists of various types of features. These features are unique due to the writer’s characteristics and individuality, which challenges the identification process. Some features do not provide useful information and may cause to decrease the performance of a classifier. Thus, feature selection process is implemented in WI process. Feature selection is a process to identify and select the most significant features from presented features in handwriting documents and to eliminate the irrelevant features. Due to the WI framework, discretization process is applied before the feature selection process. Discretization process was proven to increase the classification performances and improved the identification performance in WI. An algorithm and framework of Improved Random Forest (IRF) tree was applied for feature selection process. RF tree is a collection of tree predictors used to ensemble decision tree models with a randomized selection of features at each split. It involved Classification and Regression Tree (CART) during the development of tree. Important features are measured by using Variable Importance (VI). While Mean Absolute Error (MAE) values use to identify the variance between writers, VI value was used for splitting process in tree and MAE value is to ensure the intra-class (same writer) invariance is lower than inter-class (different writer) invariance because lower intra-class invariance indicates accuracy to the real author. Number of selected features and the classification accuracy is used to indicate the performances of feature selection method. Experimental results have shown that the performances of IRF tree in discretized dataset produced third feature (f3) as the most important feature with average classification accuracy 99.19%. For un- discretized dataset, first feature (f1) and third feature (f3) are the most important features with average classification accuracy 40.79%

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach

We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., changing any subset of the inputs does not change the decision). We (1) demonstrate how this framework may be used to provide explanations for decisions made by general, data-driven AI systems that may incorporate features with arbitrary data types and multiple predictive models, and (2) propose a heuristic procedure to find the most useful explanations depending on the context. We then contrast counterfactual explanations with methods that explain model predictions by weighting features according to their importance (e.g., SHAP, LIME) and present two fundamental reasons why we should carefully consider whether importance-weight explanations are well-suited to explain system decisions. Specifically, we show that (i) features that have a large importance weight for a model prediction may not affect the corresponding decision, and (ii) importance weights are insufficient to communicate whether and how features influence decisions. We demonstrate this with several concise examples and three detailed case studies that compare the counterfactual approach with SHAP to illustrate various conditions under which counterfactual explanations explain data-driven decisions better than importance weights

DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters

When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score.Comment: 13 pages, 5 figures, accepted by 2017 ECML PKD

Learning salience amoung [sic] features through contingency in the CEL framework

Determining which features in an environment are salient given a task, salience assignment, is a central problem in Machine Learning. A related phenomenon, contingency (the conditions under which relative salience among environmental features is acquired), is central to learning and memory in animal psychology. This paper presents an analysis of a set of empirical data on contingency and an algorithm for the salience assignment problem. The algorithm presented is implemented in a working computer program which interacts with a simulated environment to produce contingent associative learning corresponding to relevant behavioral data. The model also makes specific empirical predictions that can be experimentally tested