Feature Interaction Analysis Research Articles

Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis

ABSTRACT This study focuses on addressing housing gaps in populous southern countries by emphasizing the importance of accurate early cost estimation in construction projects. It compares individual cost prediction models (Decision Tree, BP Neural Network, and Support Vector Machine) with combined prediction models (BP-DT and BP-SVM) for high-rise building cost prediction. The study employs interpretative analysis methods such as the Shapley additive explanation method, partial dependence plots, and individual conditional expectation plots to explore feature interplay and improve model transparency. Results show that the BP-SVM combination algorithm significantly reduces Mean Absolute Error (MAE) compared to other models. Key factors influencing cost estimation include “Estimated project duration” and “Ground floor area”, while others like “Below-ground floors” have minimal impact. The study highlights the interaction of various feature groups on prediction outcomes. Through feature interaction analysis, it is found that the Estimated project duration and Building architecture can have some negative correlation interaction. Estimated project duration and Interior decoration may have to cancel effects Overall, it contributes to construction management by aiding investors in better-assessing project profitability, thereby enhancing investment decision efficiency and quality.

Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model

The debris flow velocity fundamentally determines its intensity, thereby rendering its prediction a crucial aspect of disaster prevention and control strategies. However, accurate velocity prediction has consistently posed significant challenges due to the intricate interplay of various influential factors. To address the limitations of existing models, an explainable multi-strategy fusion of Stacking ensemble learning is proposed. Initially, an improved snake optimization (ISO) algorithm is employed to adjust parameters within the model’s learners. Benchmark function comparison tests are then conducted to validate the reliability of the ISO algorithm. Subsequently, a learner selection method based on predictive performance and degree of difference is established to facilitate the selection of basic learners and meta-learners. This leads to the construction of the Stacking ensemble learning model, achieved through the integration of parameter optimization strategies from the improved swarm intelligence algorithm strategy, the error weighting strategy, and the decomposition strategy. To assess the model, a case study of the Jiangjiagou Gulley debris flow is undertaken, focusing on the prediction of the debris flow velocity. The results demonstrate high predictive accuracy, with RMSE, MAE, and MAPE values of 0.19, 0.17, and 2.46% respectively. Furthermore, under the SHAP framework, global and local explanations of the predictions are provided. Through feature importance analysis, the bed slope gradient is identified as the most crucial feature in the velocity prediction of the Jiangjiagou Gulley debris flow. Coupling effects and contributions of input features to the debris flow velocity prediction are further analyzed and explained through feature interaction analysis and single sample analysis. This study not only provides a new method for debris flow velocity prediction but also provides guiding suggestions for debris flow monitoring and control.

Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems

This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini’s effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context. BukaGini’s performance is evaluated across four diverse datasets commonly used in IDS research: NSLKDD (22,544 samples), WUSTL EHMS (16,318 samples), WSN-DS (374,661 samples), and UNSWNB15 (175,341 samples), amounting to a total of 588,864 data samples. The evaluation encompasses key metrics such as stability score, accuracy, F1-score, recall, precision, and ROC AUC. Results indicate significant advancements in IDS performance, with BukaGini achieving remarkable accuracy rates of up to 99% and stability scores consistently surpassing 99% across all datasets. Additionally, BukaGini demonstrates an average reduction in dimensionality of 25%, selecting 10 features for each dataset using the Gini index. Through rigorous comparative analysis with existing methodologies, BukaGini emerges as a promising solution for feature interaction analysis within cybersecurity applications, particularly in the context of IDS. These findings highlight the potential of BukaGini to contribute to robust model performance and propel intrusion detection capabilities to new heights in real-world scenarios.

SHAP-based insights for aerospace PHM: Temporal feature importance, dependencies, robustness, and interaction analysis

This research addresses a critical challenge in aerospace engineering: enhancing the interpretability of machine learning models for predictive maintenance. By integrating SHapley Additive exPlanations (SHAP), our approach decodes the relative importance of sensor-derived features, providing an analytical foundation for understanding engine degradation signals. We delve into the temporal shifts in feature relevance, unveiling the variable impact of sensor data over operational cycles. A rigorous assessment of SHAP's robustness further strengthens the reliability of our interpretive models in the face of data perturbations. Additionally, our nuanced analysis of feature interplay offers a comprehensive view of the factors influencing engine performance predictions. These methodological advancements equip engineers with precise, actionable insights for preemptive maintenance scheduling, directly contributing to the enhancement of aircraft safety and efficiency. The study's implications extend beyond theoretical analysis, offering a pragmatic blueprint for the application of SHAP in the ongoing pursuit of model transparency and maintenance optimization in the aerospace sector.

Comprehensive analysis of heart rate variability features for accurate prediction of paroxysmal atrial fibrillation

ObjectiveParoxysmal atrial fibrillation (PAF) is the first stage of atrial fibrillation and can escalate to a chronic stage without early intervention. This study aims to explore the predictive potential of heart rate variability (HRV) features for PAF and investigate their time variabilities and correlations to enhance prediction accuracy. MethodWe analyzed HRV data extracted from 30-min ECG recordings in the Paroxysmal Atrial Fibrillation Prediction Database (AFPDB). A total of 57 features were extracted from each 5-min segment, including time-domain, frequency-domain, and nonlinear features. Statistical tests, recursive feature elimination, feature inclusion and Bayesian networks were employed for feature optimization, and supervised learning classifiers and 10-fold cross validation were utilized for PAF prediction. ResultsThe random forest classifier demonstrated the best predictive performance, with accuracy, sensitivity, specificity, and F1 score reaching 98.0%, 96.7%, 100.0% and 0.980, respectively. Time-domain features consistently showed excellent classification abilities. Bispectral features and Poincaré plot features performed well in specific segments. The analysis of feature interactions provided valuable insights into the relationships among different feature groups. ConclusionOur comprehensive analysis of HRV features reveals their significance in predicting PAF and emphasizes the importance of considering feature time variabilities and correlations. The proposed prediction method with novel feature optimization strategies shows promising results in enhancing prediction accuracy. SignificanceThe accurate prediction of PAF using HRV features has important clinical implications for early detection and intervention. The findings contribute to the advancement of AF prediction and personalized healthcare strategies.

Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction.

Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.

Development potential of nanoenabled agriculture projected using machine learning

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms.

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability.

BackgroundMicrosatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice.MethodsIn this study, interpretable pathological image analysis strategies are established to help medical experts to identify MSI. The strategies only require ubiquitous hematoxylin and eosin–stained whole-slide images and perform well in the three cohorts collected from The Cancer Genome Atlas. Equipped with machine learning and image processing technique, intelligent models are established to diagnose MSI based on pathological images, providing the rationale of the decision in both image level and pathological feature level.FindingsThe strategies achieve two levels of interpretability. First, the image-level interpretability is achieved by generating localization heat maps of important regions based on deep learning. Second, the feature-level interpretability is attained through feature importance and pathological feature interaction analysis. Interestingly, from both the image-level and feature-level interpretability, color and texture characteristics, as well as their interaction, are shown to be mostly contributed to the MSI prediction.InterpretationThe developed transparent machine learning pipeline is able to detect MSI efficiently and provide comprehensive clinical insights to pathologists. The comprehensible heat maps and features in the intelligent pipeline reflect extra- and intra-cellular acid–base balance shift in MSI tumor.

Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database.

Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States.

PREDICTION OF LEFT VENTRICULAR REMODELING IN PATIENTS WITHOUT CARDIOVASCULAR DISEASE THROUGH MACHINE LEARNING: AN ECG-BASED APPROACH

Objective: Cardiac remodeling -even at early stages-is considered an important aspect of cardiovascular disease progression and is therefore emerging as a significant therapeutic target. We used machine learning (ML) techniques and investigated several ML classifiers built on basic clinical parameters and ECG features for detecting left ventricular remodeling (LVR), even at the early stages before the onset of LVH in a population without cardiovascular disease Design and method: We enrolled 475 subjects with and without essential hypertension and no other indications of cardiovascular disease. Based on left ventricular mass index (LVMi) and relative wall thickness (RWT), calculated using echocardiography, the subjects were classified into 3 groups; those with normal geometry, concentric remodelling, concentric and eccentric left ventricular hypertrophy (LVH). We trained a Random Forest, i.e. a nonlinear predictive ML model suitable for panel data, to classify subjects either in the normal group, or in the other three combined (LVH and both types of left ventricular hypertrophy-LVH). We also perform feature importance and feature interaction analysis by calculating SHAP values, a game theoretic tool that helps in interpreting the model's predictions. Results: Hypertension, age, and body mass index (BMI) were the most significant features, as expected. Additionally, a combination of BMI and Sokolow criteria as well as the areas under the QRS complex and the QTc duration, among others, were also important. The age of 65 marked a sharp change in the risk of being classified with LV remodelling and LVH. The random forest model was able to distinguish normal subjects from the ones with LVR and LVH with accuracy 80%, specificity 65% and sensitivity 91%. Conclusions: We found promising clinical and ECG features in the direction of machine based assistance in the detection of LVR even at early stages before the onset of LVH. Strategic and innovative solutions are needed to improve hypertension management, especially in primary care settings and our data indicate that ML may improve risk stratification of hypertensive population with simple clinical approaches.

Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.

Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). The authors trained supervised ML models to classify patients with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow‐Lyon voltage, QRS‐T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from CR+LVH combined, with 87% accuracy on an unseen test set, 75% specificity, 97% sensitivity, and area under the receiver operating curve (AUC/ROC) equal to 0.91. The authors also trained our model to classify NG and CR (NG + CR) against those with LVH, with 89% test set accuracy, 93% specificity, 67% sensitivity, and an AUC/ROC value of 0.89, for a 0.4 decision threshold. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, and ML may enable progress in this direction.

A Flexible Approach for Modelling and Analysis of Feature Interactions in Service-Oriented Product Lines

A Flexible Approach for Modelling and Analysis of Feature Interactions in Service-Oriented Product Lines

Feature interaction in a federated communications-enabled collaboration platform

Existing online collaboration tools and platforms provide basic communications integration and the ability to include some real-time information sources. Moreover, users prefer to be able to choose which collaboration tool they use for a given interaction, and over the course of long-term collaboration, will typically use a variety of tools, including email, instant messages, wikis, blogs, web conferences, and shared documents.For enterprise use there is a need to integrate the various tools as well as link with existing intelligent communication systems to support long-term collaborations in a variety of ways. Due to the number and different nature of collaboration services used in enterprises today, building such a federated collaboration platform is challenging. Collaboration tools differ in terms of storage model, APIs, content organization, addressing, formats, user authentication, and user interface. By the very nature of such systems they include a large number of independently developed features and services and thus provide a strong potential for feature interactions. This paper presents novel work on feature interaction analysis in collaboration environments and presents an approach to detect and resolve such interactions where the collaboration space is used as a communication endpoint.In this paper ConnectedSpaces is used as a basis to carry out a detailed analysis of feature interaction problems in collaboration environments. ConnectedSpaces is a new model for federated collaboration environments. Like a number of existing systems, ConnectedSpaces uses a collaboration space as the basic construct. ConnectedSpaces enables the user to work directly in the client application of their choice; this is illustrated with plug-ins for MS Outlook, Internet Explorer and Skype.This paper presents distinctive characteristics of ConnectedSpaces, including views, spaces as communication endpoints, space persistence and structuring, and embedded objects. Using these features, new types of feature interactions for collaboration platforms are categorized and analyzed. The work presented in this paper focuses on handling feature interactions which are caused by using ConnectedSpaces as a communication endpoint with enterprise communication platforms. This work is novel as it is the first investigation into feature interactions with collaboration platforms. Our approach uses a runtime feature interaction technique which can cope with features being provided by different organizations.

Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition

Understanding what object features the visual system relies upon, when engaged in visual object recognition, is a longstanding challenge in psychophysical studies of visual perception. Although successful approaches have been developed to address this issue (e.g., image classification methods [1]), they have a major limitation: they can estimate the relationship between input visual images and output behavioral responses only under the assumption of a linear observer model (e.g., an observer performing a weighted sum of the information carried by individual pixels within an image). For instance, one popular image classification approach (named the Bubbles method [2]) recovers the salient features used by an observer to identify an object, by presenting the target object partially occluded by opaque masks punctured by transparent windows, and then averaging the trials yielding to correct object identification. However, this method cannot tell whether multiple salient features in an object interact non-linearly (e.g., whether those features need to be simultaneously visible for the object to be correctly identified). Here we propose two approaches to overcome this limitation. As a preliminary step, we simulated observers with various recognition strategies. Each observer had to discriminate bubbles-masked input images of two different objects, by comparing them with either linear or non-linear object templates. One of the object templates contained two features, whose evidence in any given input image was computed as the dot-product between the image and the features themselves. Finally, the overall template evidence was computed either as: 1) the product of the two feature evidences (AND-like feature interaction); 2) the maximum of the feature evidences (OR-like feature interaction); or 3) the sum of the feature evidences (linear interaction). The simulated observers served as benchmarks to validate two different feature-interaction analysis approaches. The first approach consisted of measuring the mutual information between the product/max/sum of every pair of pixels in the object image and the simulated observers’ responses. This approach successfully recovered the simulated feature interaction strategy, which was shown to convey significantly higher information about the observer’s response, as compared to the two other alternative strategies (permutation test; p < 106). The second approach consisted in fitting a model to the AND-like, non-linear observer’s responses, which could predict responses to novel input images. As a model we chose a regularized logistic regression classifier, which was fed with both individual pixel values and the product of pixel pairs. The classifier successfully generalized to unseen input images (training performance=100%, test performance=87%) and its maximal weights matched/captured the simulated feature interaction (e.g., pixels’ products had maximal weights). Finally, we also started applying sparse classifiers[3], which, by relying on diluted discrete weights, could potentially yield a cleaner (i.e., more parsimonious) estimate of salient object features and their interaction. In summary, our simulations show that both information theoretic and classifiers-based approaches can recover the type and the strength of the interaction among the salient features of an object. We are now working to apply these methods to analyze data obtained in a Bubbles-based behavioral study of rat visual object recognition.

Feature Interaction Aware Test Case Generation for Embedded Control Systems

The growing number and increased coupling of functionality in embedded control systems, e.g. in the automotive domain, leads to complex networks of interacting features and a wide range of variants. Hence, today's software development processes must include systematic approaches to analyze the functional correctness of system specifications and implementations. Model-based testing is of particular importance for embedded software systems as the test cases can be performed at the real system under test and failures arising from the interaction of software and hardware can be discovered. As features are usually designed in a modular and isolated way, unexpected and undesired behavior caused by unintended interferences of insufficiently synchronized and even contradicting features concurrently active in the system often remains undetected which may lead to serious safety problems. To overcome such feature interactions at system integration level is tedious as it leads to a voluminous number of unmanageable test cases. We describe a model-based approach for efficiently generating test cases that particularly aim at feature interaction analysis. We first characterize feature interaction in a formal way based on a rigorous functional architecture model, and describe how to detect potential feature interactions. For test case generation, behavioral models like Statecharts and according coverage criteria can be used as usual, but only those models are integrated into the test model, that contribute to one of the features under consideration. This leads to a trade off between comprehensive test coverage to find possible flaws caused by interacting features, and yet still a reasonable number of test cases. The steps of the approach are illustrated be means of a case study from the automotive domain.

A template-based approach for the generation of abstractable and reducible models of featured networks

We investigate the relationship between symmetry reduction and inductive reasoning when applied to model checking networks of featured components. Popular reduction techniques for combatting state space explosion in model checking, like abstraction and symmetry reduction, can only be applied effectively when the natural symmetry of a system is not destroyed during specification. We introduce a property which ensures this is preserved, open symmetry. We describe a template-based approach for the construction of open symmetric Promela specifications of featured systems. For certain systems (safely featured parameterised systems) our generated specifications are suitable for conversion to abstract specifications representing any size of network. This enables feature interaction analysis to be carried out, via model checking and induction, for systems of any number of featured components. In addition, we show how, for any balanced network of components, by using a graphical representation of the features and the process communication structure, a group of permutations of the underlying state space of the generated specification can be determined easily. Due to the open symmetry of our Promela specifications, this group of permutations can be used directly for symmetry reduced model checking. The main contributions of this paper are an automatic method for developing open symmetric specifications which can be used for generic feature interaction analysis, and the novel application of symmetry detection and reduction in the context of model checking featured networks. We apply our techniques to a well known example of a featured network - an email system.

Feature interaction detection by pairwise analysis of LTL properties—A case study

A Promela specification and a set of temporal properties are developed for a basic call service with a number of features. The properties are expressed in the logic LTL.Interactions between features are detected by pairwise analysis of features and properties. The analysis quickly results in both state-space and property case explosion. To overcome this state-spaces are minimised, model checking results generalised through symmetry and bisimulation, and analysis performed automatically using scripts. The result is a more extensive feature interaction analysis than others in the field.

Fundamental Nonmodularity in Electronic Mail

Electronic mail (email) systems have grown in complexity to the point where their reliability and usability are coming into question. The authors of individual email components are widely distributed in both time and space, bound together only by message structure and transfer protocol specificationss consequently filters, autoresponders, and various security features may interact in unanticipated and, often, incomprehensible ways. This paper describes a formalism for modeling and composing specifications of email features, and a feature interaction detection methodology based in part on human intuition and in part on simulation and formal test coverage. The appendix lists 27 interactions found from applying the methodology to ten common email features, a result of independent interest considering the age and relative maturity of the email domain. The paper then proceeds to categorize the interactions according to their impact on the design of the system. This includes a design study of the most natural ways to fix the undesirable behaviors. From this we can infer non-modular dependencies among the features, leading to the result that 9 of the 10 features must be revised (and a custom user interface must be built for them) after they are composed and after feature interactions are detected. This pervasive nonmodularity shows that feature interaction analysis is necessary to optimizing the correctness of an email system design.

Modeling of manufacturing feature interactions for automated process planning

This paper analyzes manufacturing feature interactions and presents algorithms for handling feature interactions in manufacturing process planning. A feature precedence network, which specifies the constraints for the machining sequence, and an AND-OR graph, which describes alternative representations, are built. Application of required/optional volumes, the AND-OR graph, and the feature precedence network in manufacturing process sequencing is then explained. Required and optional volumes are defined and their use for analysis of interactions of various machining feature types is explained. Case studies are presented to explain volumetric interactions of holes, slots, and pockets with various dimensions, positions, and orientation in the part geometry. The analysis provides insight into which process-sequencing constraints may be resolved before actual process planning and which have to be postponed to be resolved in final process sequencing. A three-phase algorithm for incorporating of volumetric feature interactions into manufacturing process planning is proposed. The first phase is creation of an AND-OR graph of alternative feature representations. The second phase is building a feature precedence network for a given part as a set of machining sequence constraints. The third phase is analysis of feature interactions for performing geometric updates of machining features during the process sequencing stage.